Supply Chain: The Way to Flat Organisation

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Supply Chain, The Way to Flat Organisation


Edited by

Yanfang Huo and Fu Jia

I-Tech

IV

Published by In-Teh

In-Teh is Croatian branch of I-Tech Education and Publishing KG, Vienna, Austria. Abstracting and non-profit use of the material is permitted with credit to the source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside. After this work has been published by the In-Teh, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. © 2008 In-teh www.in-teh.org Additional copies can be obtained from: [email protected] First published December 2008 Printed in Croatia

p. cm. ISBN 978-953-7619-35-0 1. Supply Chain, The Way to Flat Organisation, Yanfang Huo and Fu Jia

Preface With the ever-increasing levels of volatility in demand and more and more turbulent market competition, there is a growing recognition that individual business no longer compete as stand-alone entities but rather as supply chains. And supply chain management (SCM) has been both an emergent field of practice and an emerging academic domain to help to firms to satisfy the customer needs more responsively, with improved quality, reduction cost and higher flexibility. According to one American professional association, SCM can be defined as a field which encompasses the planning and management of all activities involved in sourcing, procurement, conversion, and logistics management activities. Importantly, it also includes coordination and collaboration with channel partners, which can be suppliers, intermediaries, third-party service providers, and customers. In essence, SCM integrates supply and demand management within and across companies. Facing this challenge, the companies should make some fundamental changes, which involves not only the total transparency through Information sharing, but also process integration, organizational structures reengineering, and performance measures change as well. Only the organizations will win who can better structure, co-ordinate and manage the relationships with their partners in a network committed to better, closer and more agile relationship with the final customers. Although lots of researches and practices have been devoted in this field, neither perspective is fully mature but each has considerable promise. Mainly concerned on the operation and control of supply, the book collected some latest development and findings. It consists of 20 chapters, each addressing a certain aspect of supply chain management, including the application and development ICT and the RFID technique in SCM, the new mathematical tools and techniques for SCM modeling and control, and some emerging issues in the academic research and practices of supply chain management. Each chapter gives the reader background information on a subject and proposes an original solution. This should serve as a valuable tool for professionals in this interdisciplinary field. Hopefully, readers will contribute their own discoveries and improvements, innovative ideas and concepts, as well as novel applications and business models related to the field of supply chain management. A brief introduction to each chapter is summarized in the following. Chapter 1 is about the optimal inventory control strategy of a serial supply chain. A two-level model was suggested, one level to determine the optimal control strategy using a nonlinear integer-programming model solved by intelligent algorithms of GA, random-PSO and PEA, and the other to obtain the performance measurements of the optimized supply chain by simulation of the general push/pull model. Chapter 2 explored a synergistic approach towards autonomic event management in supply chains aiming at improving the qualities of supply chain event management (SCEM), especially with regard to approaching self-X properties and automation. The

VI

holistic approach leveraged various computing paradigms of granular, semantic web, service-oriented, space-based, etc. Chapter 3 proposed the use of enterprise input-output (EIO) models to represent and analyze physical and monetary flows between production processes, including logistics ones. Based on the use of EIO models, a set of complete and complementary tools able to analyze the problem according to different perspectives and point of views are presented. Chapter 4 focused on the operational activities of the supply chain dynamics, and proposed dynamic models for multipurpose systems considering production ratios. By introducing new parameters, the models go from a simple linear supply chain based on material flows to a nonlinear one considering orders handling and based on traffic flow theory, both are applied to two simulation case studies. Taking optimization based e-sourcing models as objective, Chapter 5 reviewed three popular e-sourcing techniques with their underlying mathematical programming models that are used to solve the winner determination problems, and presented two future directions also: global sourcing and robust sourcing. RFID has thought to be “the first important technology of the twenty-first century”, and lots of researches have been done in this field. Chapter 6 suggested a Domain Engineering Process for RFID Systems Development in Supply Chain, which defines a systematic process to perform domain engineering which includes the steps of domain analysis, domain design, and domain implementation. In Chapter 7, some issues of the return policies and collaboration in supply chain are reviewed, involving an overview of the benefits and costs of returns policies, the different kinds of returns policies that are required to coordinate the supply chain for the different types of products and the impact of demand uncertainty and retailing competition on returns policies. Chapter 8 focuses on the problem of managing at the operational level supply chains, and described it by a modular model based on the first order hybrid Petri net formalism, which can effectively describe the operational management policies and the inventory control rules, and enables the designer to impose an optimal SC dynamics according to given objective functions. Chapter 9 described a statistical physics approach to understanding the supply chain oscillations, models of both normal modes and External interventions are demonstrate by inventory oscillations model and a fluid-flow model separately. It is the first time that the general approach together with its applications has been assembled in one place, along with a number of possible extensions. Information Technique is one of the most enablers of SCM development. In Chapter 10, a framework that enhances the agility of SCM with IT is presented. A simulation known as The Trading Agent Competition: Supply Chain Management Game (TAC SCM) was sponsored by a group of universities and research centers to compete against each other to prove mechanisms for supply chain situations since 2003. Chapter 11 presented the deepest analysis about the construction of the Tiancalli agents since 2005, intending to describe the effort and experience during the three years participating on TAC SCM. Chapter 12, investigating the methods of supply chain integration for manufacturing industry in the background of China, proposed a three-echelon theoretical framework for

VII

supply chain integration based on Thorn’s model and presented the relative key techniques of each level. Referring to develop integrated supply chains significantly more flexible, responsive and agile than traditional supply chains, Chapter 13 discussed two new approachesDynamic Agility Index and Fuzzy Association Rule Mining - for modeling and evaluating agility in dynamic integrated supply chains. Chapter 14 described the design and implementation of the MIDAS supply chain system by Using Web Services and the Service Oriented Architecture. In Chapter 15, a distributed supply chain planning system for multiple companies with Limited Local Information using an augmented Lagrangian relaxation method has been proposed. While by introducing Fuzzy mixed integer Linear Programming to tactical Supply Chain Planning, a multi-echelon, multi-product, multi-level and multi-period SC planning model was established in Chapter 16, given lack of knowledge (demand, process and supply uncertainties). Chapter 17 explored the research issues on collaborative product design and development based on CM principles, which then be introduced in four areas separatelyconfiguration identification, configuration change control, configuration status accounting, and configuration audits. Chapter 18 illustrated the RFID and EPC potential for business processes and presented RFID@B2B, a new method to improve the supply chain performances using RFID technology. Chapter 19, focusing on the issues and potential solutions for a range of security vulnerabilities of RFID systems, analyzed the underlying vulnerabilities that exist in RFID systems, illustrated the threats of possible attacks, and provided corresponding countermeasures. In Chapter 20, aiming at the problems in traditional knowledge retrieval, an approach is put forward to supply chain knowledge management construction by introducing ontology, which consists of construction of domain ontology, formalization of ontology model, and development of supply chain knowledge management system based on ontology. We would like to thank all the authors for their excellent contributions in the different areas of supply chain management. It is their knowledge and enthusiastic collaboration that lead to the creation of this book, which we are sure that will be very valuable to the readers. December 2008

Editors

Yanfang Huo

School of Management, Tianjin University P.R. China

Fu Jia

Cranfield School of Management, Cranfield University UK

Contents Preface 1. Optimal Control Strategy for Serial Supply Chain

V 001

Min Huang, W.H.IP, Xingwei Wang and Jianqing Ding

2. A Synergistic Approach towards Autonomic Event Management in Supply Chains

021

Roy Oberhauser

3. Managing Logistics Flows Through Enterprise Input-Output Models

033

V. Albino, A. Messeni Petruzzelli and O. G. Okogbaa

4. Dynamic Analysis and Control of Supply Chain Systems

053

Alejandro Rodríguez-Angeles, América Morales Díaz and Arturo Sánchez

5. Optimization Based e-Sourcing

073

Kameshwaran Sampath and Lyès Benyoucef

6. A Domain Engineering Process for RFID Systems Development in Supply Chain

103

Leonardo Barreto Campos, Eduardo Santana de Almeida, Sérgio Donizetti Zorzo and Silvio Romero de Lemos Meira

7. Return Policies and Coordination of Supply Chain

127

Mabel C. Chou

8. Operational Management of Supply Chains: A Hybrid Petri Net Approach

137

Mariagrazia Dotoli, Maria Pia Fanti and Agostino Marcello Mangini

9. A Physics Approach to Supply Chain Oscillations and Their Control

163

Ken Dozier and David Chang

10. Utilizing IT as an Enabler for Leveraging the Agility of SCM Mehdi Fasanghari and S. K. Chaharsooghi

183

X

11. Development and Evolution of the Tiancalli Project

193

Macías Galindo Daniel, Vilariño Ayala Darnes and López y López Fabiola

12. A Framework and Key Techniques for Supply Chain Integration

215

Yanfang Huo, Xinyue Jiang, Fu Jia and Bingguang Li

13. New Approaches for Modeling and Evaluating Agility in Integrated Supply Chains

237

Vipul Jain and Lyes Benyoucef

14. Managing and Integrating Demand and Supply Using Web Services and the Service Oriented Architecture

259

Firat Kart, Louise E. Moser and P. M. Melliar-Smith

15. Distributed Supply Chain Planning for Multiple Companies with Limited Local Information

283

Tatsushi Nishi

16. Applying Fuzzy Linear Programming to Supply Chain Planning with Demand, Process and Supply Uncertainty

299

David Peidro, Josefa Mula and Raúl Poler

17. Research Issues on Collaborative Product Design and Development

323

Jiun-Yan Shiau

18. Improvement of Supply Chain Performances Using RFID Technology

339

Cornel Turcu, Cristina Turcu and Adrian Graur

19. RFID Technology, Security Vulnerabilities, and Countermeasures

357

Qinghan Xiao, Thomas Gibbons and Hervé Lebrun

20. Ontology and Its Application in Supply Chain Information Management Zetian Fu, Jun Yue and Zhenbo Li

383

1 Optimal Control Strategy for Serial Supply Chain Min Huang1,2, W.H.IP3, Xingwei Wang1 and Jianqing Ding1 1 College

of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 2Key Laboratory of Integrated Automation of Process Industry, Northeastern University, Shenyang, Liaoning, 3Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, 1,2P.R.China 3Hong Kong

1. Introduction With the emerging of global economy and the development of the technology in computer and communications, the enterprises are facing to new opportunities but also more challenges, which led to the concept of SC (Supply Chain). By controlling and collaborating each part of the supply chain, SCM (Supply Chain Management) reaches the aim of reducing the cost, improving the quality as well as service level, and so on, further enhances the integrated competition ability of the whole supply chain. SCM includes many aspects of the management activity, and the inventory management is one of the key aspects among them. In this chapter, the research focuses on inventory control strategy optimization related to activities, such as the purchase, production, storage and transportation of material, work in process and finished goods inventory within up-stream and down-stream enterprises along the serial supply chain. The main research aspects are as follows: 1. To review the state of art of inventory control strategies of supply chain. 2. Supply chain inventory control strategy optimization is based on the simulation of supply chain inventory system. So, in this chapter, the general model of serial supply chain inventory control strategy is addressed. 3. Model for single objective control strategy optimization is established, which describes the optimization problem of serial supply chain inventory control strategy with the objective of minimum cost and the constraint of the customer service level and average input standard deviation. 4. The algorithm of GA (Genetic Algorithm), Random-PSO (Particle Swarm Optimization) and PEA (Pheromone Evolutionary Algorithm) are designed for the model. Simulation studies suggested that each of the algorithms can solve this problem efficient, and Random –PSO algorithm is most efficient one.

2


2. The general control model for serial supply chain 2.1 The different control strategies for serial supply chain Under globalization and the rapid development of computers and information technology, all enterprises face new chances as well as more challenges. This breeds the concept of a serial supply chain, a value-added chain that is composed of a series of enterprises: raw material suppliers, parts suppliers, producers, distributors, retailers, and transportation enterprisers. Clients finally get their products, which are manufactured and handled systematically by the enterprises of the chain, started from either the raw material suppliers or the parts suppliers. This series of activities are the total activities of a complete serial supply chain, that is, from the supplier’s suppliers to the clients’ clients [1, 2]. SCM aims at decreasing the system cost, increasing the product quality, and improving service level by collaborating and controlling the conduct of each entities of the supply chain. The goal is to upgrade the overall competitive ability of the whole system. Hence, the inventory management of the serial supply chain is important. The inventory control strategy of an enterprise affects the cost and the revenue indirectly. Therefore, the target of an optimal inventory is both to maximize the degree of clients’ satisfaction and to minimize the overall cost [3]. Inventory decouples the supplying, producing, and selling processes of an enterprise. Each process operates independently. This helps to reduce the effect that comes from the variation of demand forecast, and makes good use of resources when variation happened due to demand changes and market changes. On the other hand, capital is needed for setting up an inventory. The cost includes the capital used for inventory and products in process, the space used for inventory, the expenses on management, maintenance, and discarding of defected products. Inappropriate inventory management even affects the operation efficacy of the enterprise. The characteristic of uncertainty of a supply chain increases the overall inventory of the whole chain system. It also brings unnecessary cost to the node enterprises of the supply chain. In order to avoid the “bullwhip effect” caused by the uncertainty of demand and supply, the traditional inventory strategy has to be revised. Inventory control of a supply chain can be improved by strategies like shared technology, contract system, and integrated enterprises. Thus, the competitive ability of a supply chain is enhanced. Generally speaking, there are two kinds of production inventory systems: the push and the pull system. The current worldly popular production inventory control systems of Manufacturing Resource Planning (MRPII) and Just-in-time (JIT) belong to the system, respectively. The push production control system adopts a central control method and organizes production by forecasting the future demand. Therefore, production lead time is estimated in advance. The pull production control system adopts a distributed control method and production is organized according to the real demand [4]. Each method has its own advantages [5, 6]. Peoples try to combine the two methods to attain better performances [7-9], CONWIP (CONstant Work in Process), proposed by Spearman et al. in 1990, is an example of combined push/pull control method [10]. In 2001, Gaury et al. proposed a methodology to customize pull control systems[11]. In 2003, Ovalle and Marquez suggested the model of CONWIP control system for a serial supply chain and also shows the corresponding simulation analysis [12]. But, up till this moment, all researches on control system of supply chain only deal with single specific control strategy like the push system, the pull system, the classic combined push/pull CONWIP control system[10,13-14], or the

3

Optimal Control Strategy for Serial Supply Chain

simple combination of push and pull system. A generally used model of push/pull control strategy and its research is still absent. The aim of the research is to establish a generally used method of the inventory system of a serial supply chain to replace those traditional classic control systems. In the systems of Kanban and CONWIP, system performances rely on the card quantities. Similarly, in the serial push/pull system, distribution of circulating cards (the number of circulating cards at different stages) determines the control model, guides the production time and production quantity of the generally used system. In the push system, the card numbers between each pair of nodes on the SC is infinite. Therefore, determination of circulating cards becomes a key factor affecting the operating efficacy of a generally used inventory system of a serial supply chain. This chapter proposes an optimal control model that tackles a series of multi-stages of inventory control system of a supply chain. The model is based on the combination of nonlinear integer programming and the generally used push/pull system of the inventory of a serial supply chain. It determines the distribution of circulating cards by integrating the intelligent algorithms and simulation analysis. Both the case studies have proved that the results from intelligent algorithms are reasonable and effective. 2.2 The general control model for serial supply chain Figure 1 shows that there are n nodes on the whole serial supply chains. Each node represents upstream/downstream node enterprises like raw material suppliers, manufacturers, distributors, retailers, and clients. Since the final target of a supply chain is to satisfy the clients’ demands, each enterprise operates its production and sales under the generally used push/pull inventory system control. That is, production of each node enterprise is affected by the raw material supply of the upstream enterprise and the demand of the downstream enterprise. One important goal of the supply chain is to reach a win-win status, to maximize the profits of the whole chain instead of any individual enterprise. In order to control the quantity of products in process of the chain, there is a fixed product standard on the feedback for the demand on upstream enterprise i from downstream u

enterprise j and marks it as card number K ij . Only when the real quantity of products in

Fig. 1. The general control model for serial supply chain process of node enterprise i is less than the forecast product quantity of each downstream enterprise, then it is allowed to proceed with the manufacture. Once the product of a unit is allowed to be manufactured by node enterprise i, the node’s free card is attached to its manufactured container, then the product has finished processing it is sent to the next node enterprise i+1. The attached circulating card is detached and returns to node enterprise i as a

4

Supply Chain, The Way to Flat Organisation u

free card and authorizes further manufactures of other products. When the value of K ij is ∞, it means that there is no feedback control on upstream node i from downstream node j. If there is no feedback control on node i from all downstream nodes, then node i is under the push control. The commonly used push/pull system has the following properties and assumptions: 1. Clients’ demands satisfy the normal distribution with upper and lower bound. 2. The supply chain produces only one kind of product. 3. All upstream enterprises can obtain the return cards from any downstream manufacturing enterprise node without delay (no return delay). 4. There are sufficient materials for the initial node enterprise of the supply chain. Description of the Control Strategy In order to describe the control strategy, two definitions are needed. Definition 1: The card number matrix K ⎡ K11 K12 ..........K1n ⎤ ⎢ ⎥ ⎢ ⎥ K ...... K 22 2n K= ⎢ ⎥ ⎢. ⎥ ⎢ ⎥ .. ... K nn ⎥⎦ ⎢⎣.

(1)

u

The element K ij of matrix K represents the card number in the control cycle sent by u

downstream node j to upstream node i. K ij = ∞ implies that there is no pull control from node j to node i. Since all nodes are under the control of their downstream nodes, so the lower triangular matrix of K is meaningless. Thus, values of these elements are fixed as –1. So, we have equation (2): ⎧⎪−1 (i > j ) K ij = ⎨ u (i ≤ j ) ⎪⎩1 ~ K ij + 1 u

(2) u

where K ij represents the upper limit of the card number, K ij + 1 represents +∞. Definition 2: Control matrix M: matrix that describes the control model. ⎡ M 11M 12 ......M 1n ⎤ ⎢ ⎥ ⎢ M 21M 22 ......M 2 n ⎥ ⎢ ⎥ M= ⎢. (3) ⎥ ⎢. ⎥ ⎢ ⎥ ⎢ M n1M n 2 ......M nn ⎥ ⎣⎢ ⎦⎥ Matrix M in (3) shows what kind of control strategy each node of the supply chain has. If the element Mij of matrix M equals to 1, it means there is pull control on upstream node i from downstream node j. If the element Mij of matrix M equals to 0, it means that there is no pull control on upstream i from downstream node j. So we have equation (4):

5


⎧⎪1 M ij = ⎨ ⎪⎩0

( K ij ≠ ∞) ( K ij = ∞)

i≤ j

(4)

Property 1: When the sum of all elements of row i of matrix M equals to 0, there is no pull control on node i from all its downstream nodes, which means that node i is under the push control from its upstream node. 2.3 The method for determine the optimal control strategy The main problem of determining the optimal control strategy is how to choose an appropriate control strategy so that some goals of the supply chain are reached while some constraints are satisfied. A two-level model are presented to cope with this problem. The first-level model is a mathematic programming model, which optimizes the distribution of circulating cards by guaranteeing the goals of the supply chain and satisfying the certain constraints. The second-level model is the general control model for serial supply chain which is used for the analysis of the inventory system of the whole serial supply chain. Definitions of the variables for analysis is given here and the relationship among the variables is illustrated in figure 2. (1) Logistics variables Pt i quantity of products in process of node i of period t product inventory of node i of period t

Yt i S

i t

transportation quantity from node i to i +1 of period t

i t

exported quantity of product of node i of period t

O

imported quantity of raw material of node i of period t

I ti

X the real usable quantity of raw material of node i of period t (2) Technology flow variables OPt cust clients’ demands of period t i t

quantity of inflow orders of node i of period t

Dti

APC number of available cards of node i of period t i t

OPt i B

DSti TYt

quantity of processed orders (manufacturing quantity) of node i of period t quantity of accumulated orders of node n of period t

n t

i

the expected quantity of transportation of node i to i +1 of period t quantity of available overall product of node i of period t

L period of manufacturing products of node i Ls i period of transporting products of node i MLP i product-load ability of node i UC i vessel capacity of node i K matrix of circulating cards, element K ij represents the number of cards in i

the control

cycle of node i sent by node j M the control matrix mark i, it is the last node which exerts the pull control on node i

6


(3) Capital flow variables CRti quantity of cash demand of node i of period t inventory value of node i of period t

ICRti

compulsory cash-in of node i of period t

i t

R

Py

compulsory payment of node i of period t

Pmti

product price of transportation unit of node i of period t

i t

Pwipti price of products in process unit of node i of period t margin benefits of node i of period t

mrt i

CumPt accumulative benefits of node i of period t i

K ij APC t1

MLP 1

Dti APC ti

OPt1

Pt1 Ot1

1

MLP TY t

Yt1 S t1

i +1

OPt i +1

TY t i +1

i

Pt i Oti Yt i S ti I ti +1 X ti +1

I ti X ti

OPt cust

APC ti +1

Dtn

DS ti

OPt i

MLP i TY t

X t1

D ti −1

MLP n

DS tn n OPt

B

TY t n

Pt i +1 Oti +1 Yt i +1 S ti +1 X tn

Pt n

n t

S tn

Yt n

Fig. 2. Illustration of the relationship among the variables In order to analysis the supply chain, the following performance measurements are defined according to the above relationship among the variables: 1. Service level (%): (the customer satisfy percentage of the supply chain) The satisfy percentage of the last node of the supply chain is considered.

⎛ T ⎞ 100* ⎜ ∑ Dtn − BTn ⎟ ⎝ t =1 ⎠ Sl = T n ∑ Dt

(5)

t =1

2.

Standard deviation of inputs:

SDO = 3.

1 T ⎛ 1 T OPt1 / T ⎞⎟ ∑ ⎜ OPt − ∑ t =1 T − 1 t =1 ⎝ ⎠

2

(6)

Overall cost of the supply chain: T

C=

n

∑∑ CR t =1 i =1

T

i t

(7)

7


The parameters delivered from the first-level model to the second-level model are circulating card distributions of each node. The parameters delivered from the second-level model to the first-level model are the service level, standard deviation of the input, and average overall cost of the supply chain.

3. The model and algorithm for optimal control strategy problem 3.1 The model for optimal control strategy problem Usually, the goal of the supply chain operation is to minimize the total cost with the constraint of service level and input standard deviation. Under this circumstance, the firstlevel model is a nonlinear integer programming model. It optimizes the distribution of circulating cards by guaranteeing that the average overall cost of the supply chain of the system is minimized, under the constraints of service level S l and the input standard 0

deviation SDO0 . In another word, solve upper triangular elements K ij of matrix K. So, the first-level model is given as follows:

min C ( K )

st :

(8)

⎧⎪ Sl ( K ) ≥ Sl0 ⎨ ⎪⎩ SDO( K ) ≤ SDO0

(9)

K ij is the integer between 1 and K iju + 1 where i = 1," , n, j ≥ i

(11)

where K ij represents the upper bound of the card number and K ij +1 represents +∞. The first-level model is a mixture of combination problem and integer programming. If there are n node enterprises, the card number that needed to be determined will be n(n + 1) / 2 . When node enterprise i is under the control of node enterprise j in the supply chain and the upper bound of card number is K iju , there are

∏∏ ( K i =1

j ≥i

u ij

+ 1) states of searching

spaces when the constraints are not considered. To process each state is possible only if both the scale of the supply chain and the cards number are small. Heuristic algorithms are needed to solve practical problems. So intelligent algorithm is applied to solve the first-level model to determine k. The GA, Random-PSO and PEA are used in this research. A simulation is used in the second-level according to the general control model for serial supply chain to determine the performance measurements of the supply chain system under a given card distribution K, because the performance measurements of the supply chain, the cost, the service level and standard deviation of input are implicit functions of card distribution kij ( ∀i = 1," , n, j ≥ i ) . 3.2 The intelligent algorithms for optimal control strategy problem In this section, three intelligent algorithms, GA, Random-PSO and PEA are used designed for the model (8)-(11). The performance of them and the comparison among them are given.

8


3.2.1 Genetic algorithm This section give the design and analysis of GA for the above model (8)-(11) [15,16]. 3.2.1.1 Coding Integer coding is adopted considering the characteristic of the problem. Each bite represents the element of the upper triangular part of matrix K accordingly, and there are n(n + 1) / 2 bites in total. Figure 3 is the illustration of coding.

K 11

K12

K 13

K 14

…

……

……..

K nn

Fig. 3. The illustration of coding The range of each bite is from 1 to K iju + 1 where the value of K iju is determined by the ability of each production node. For instance, if the production ability of the first node of a supply chain is 30, K iju ≥ 30. If the production ability of the second node is 20, K iju will contain the control cycles of the two nodes, that is, k12u ≥ (30 + 20). Let K iju + 1 of a certain node as an infinite integer means that its downstream nodes have no limit of card number on it. Property 2: According to this coding rule, there are different upper and lower bound for each bit of individual as each bit may include different number of nodes. 3.2.1.2 Fitness Function Due to the minimizing property of the objective function, fitness functions. It is obtained by equation (12) as follows: F ( K ) = f max − f ( K )

(12)

f (K ) = C(K ) + α1 ∗[SDO(K ) − SDO0 ]+ + α2 ∗[Sl0 − Sl (K )]+

(13)

where α1 ∗ [ SDO( K ) − SDO0 ] + α 2 ∗ [ Sl 0 − Sl ( K )] are the penalties for not satisfying constraints of (9) and (10), α1 and α 2 are penalty coefficients, and f max is a given large value to guarantee that the overall fitness value is non-negative, and y>0 ⎧y [ y ]+ = ⎨ ⎩ 0 otherwise

3.2.1.3 Operators design Each initial solution is obtained by creating an integer within the range of 1 to K iju + 1 randomly for each bit. Two points crossover is adopted here. Two intersecting points are chosen from the chromosomes. Crossover is taken in the space between the two intersecting points and the rest is still inheriting the parent genes. Mutation is also applied. First, create a number between 0-1 randomly. When the number created is smaller than the mutation probability, mutation will happen by creating an integer that lies within the limits of the circulating cards randomly. The commonly used roulette mechanism is used as the choice strategy and the biggest iteration number is chosen as the criterion for algorithm termination.

9


3.2.1.4 The Elitist Mechanism In order to maintain the best chromosome of each generation, an elitist mechanism is used in the choice process. If the best chromosome of the last generation is not duplicated into the next generation, the next generation will randomly delete a chromosome so that the best chromosome of the last generation will be duplicated directly. 3.2.1.5 Numerical Analysis In order to analyze the performance of the algorithm, the problems with 4-nodes ( 1018 ), 6 nodes ( 1040 ) are analyzed here.. Example 1 is a supply chain with 4 nodes, the ability of each node enterprise is same, the custom demand is a normal distribution which mean of 4.0 and variance of 1.0, upper and lower bound are 8.0 and 0.0, the parameters are shown in table 1. The supply chain should ensure that the customer service levele is not less than 90% and the input standard deviation is less than 2.5. Node enterprise Quantity of products in process

P0i Product inventory Y i 0 Transportation quantity S i 0 Exported quantity of product O i 0 Imported quantity of raw material I i

1

2

3

4

8

8

8

8

12

12

12

12

4

4

4

4

8

8

8

8

8

8

8

8

9

9

9

9

3

3

3

3

1

1

1

1

2

2

2

2

30

30

30

30

1

1

1

1

1

1

1

1

2

2

2

2

1

1

1

1

0

The real usable quantity of raw material X i 0

Quantity of processed orders OPi 0 Period of manufacturing products Li Period of transporting products Ls i Product-load ability MLPi Container capacity UC i Margin benefits mr i t

Product price of transportation unit Pmi t

Price of products in process

Pwip0i

Table 1. The initial value of parameters for each node enterprise in 4 nodes problem According to the parameter set in table 1, the upper-lower bound of card numbers for different control segments are shown in table 2. So the size of this problem is 414 * 713 * 1062 *141= 1.602 ×1018 , while the constraint is not considered.

10


The number of nodes Lower and upper bound

1

2

3

4

1~41

1~71

1~106

1~141

Table 2. The lower and upper bound of card for 4 nodes problem To analyze the performance of the algorithm, the algorithm is run for 100 times. The best solution is the best one within 100 runs. The best rate is the rate to reach the best value within 100 runs. Taking reasonable parameter of GA, the best solution K=[∞, ∞, ∞, ∞, 16, 29, 43, 14, 31, 16] for 4 nodes problem is obtained. For the supply chain with 6 nodes enterprises, the parameters are shown in table 3, the upper-lower bound of card number for different problem are shown in table 4. Node enterprise Quantity of products in process P i

1

2

3

4

5

6

8

8

8

8

8

8

Product inventory Y i 0 Transportation quantity S i 0 Exported quantity of product O i 0 Imported quantity of raw material I i 0 The real usable quantity of raw material X i

12

12

12

12

12

12

4

4

4

4

4

4

8

8

8

8

8

8

8

8

8

8

8

8

9

9

9

9

9

9

3

3

3

3

3

3

1

1

1

1

1

1

2

2

2

2

2

2

30

30

30

30

30

30

1

1

1

1

1

1

1

1

1

1

1

1

2

2

2

2

2

2

1

1

1

1

1

1

0

0

Quantity of processed orders OPi 0

Period of manufacturing i products L Period of transporting i

products Ls Product-load ability MLP i Container capacity

UC i

Margin benefits mr i t Product price of transportation unit Pm i Price of products in process Pwip0i

t

Table 3. The initial value of parameters for each node enterprise in 6 nodes problem

11


The number of nodes Lower and upper bound

1

2

3

4

5

6

1~41

1~71

1~106

1~141

1~175

1~210

Table 4. The lower and upper bound of card for 6 nodes problem The custom demand is a normal distribution with mean of 4.0, variance of 1.0, upper-lower bound of 8.0 and 0.0, constraints condition are service level no less than 90% and the input standard deviation less than 2.5. The scale of the problem is 416 * 715 * 1064 * 1413 * 1752 * 210 = 1.951 × 1040 while the constraint is not considered. Taking reasonable population size and iterative number, the best solution K=[∞, ∞, ∞, ∞, ∞, ∞, ∞, 62, 93, 104, 170, 21, 63, 95, ∞, 23, 65, ∞, 25, ∞, ∞] for 6 nodes problem is obtained. The comparison of these two scales of problems is shown in table 5 taking reasonable parameters of NP(Number of Populations), NG(Number of Generations), PC(Probability of Crossover), PM(Probability of Mutation) obtained by simulation. Scale of the problem

1018 (4- node)

10 40 （6-ode）

NP

NG

PC

PM

Best fitness

Best rate

T(s)

200

150

1.0

0.3

892611.49

0.92

21

200

150

1.0

0.3

890379.75

0.86

39

Table 5. Comparison of the results of different scale of problems for GA Table 5 showed that, after the scale of the 6-node problem has expanded by 1022 times as compared with a 4-node problem, the CPU time is increased by 18S and the best rate is decreased by 6%. Though there are expanded complexities of the problem, the algorithm still possesses high best rate. The increase in the CPU time is within an acceptable range. Moreover, time increase is mainly caused by the influences of the expansion of scale of the problem on simulation. Thus, the performance of the algorithm is not greatly affected by the scale of the problem and it is still a fairly stable algorithm. 3.2.2 Random-PSO algorithm This section shows the solutions of the above model for single objective optimal control strategy problem by Random-PSO algorithm. PSO algorithm is bring forward by Eberhart and Kennedy in 1995[17, 18]. Originally, PSO algorithm was proposed to simulate the movement of bird swarm. People observed animal society behavior, found that in a group information share was propitious to evolvement [19], that is the basic of PSO algorithm. PSO is based on group intelligence, its unit is the swarm, then establish simple rules for each unit, so that the whole swarm could have complex characters for solving complex optimal problems. Because of the simple concept and easily to realize, PSO develop quickly in short time, soon recognized by international evolvement calculation field, and applied in many field like electric power optimization, TSP optimization, neural networks training, digital circuit optimization, function optimization, traffic accident exploration, parameter identification.

12


Classical PSO optimal algorithm described as follow: Suppose the search space is D dimension, the position of the ith particle in particle swarm is expressed as X = ( X X X ) , the speed of the ith particle is expressed i

i1,

i 2,.....

iD

as V = (V V , the best position of ith particle searched so far is denoted i1, i 2,.......ViD ) i

as P = ( P P , the best position of the whole swarm have searched so far is denoted i i1, i 2,....... PiD ) as

Pg = ( Pg1, Pg 2,....... PgD ) . For every particle, the d dimension

to the equation as follow

(l ≤ d ≤ D ) changes according

[20]:

Vid = wVid + c1r1 ( Pid − X id ) + c2 r2 ( Pgd − X id )

(14)

In equation (14), Vid denotes the speed of the ith particle at d dimension, here: w is inertia weight, c1 and c2 are acceleration constants, r1 and r2 are random number in [0, 1] used to adjust the relative importance of Pid and Pgd, so that could obtain the next movement position of the particle:

X id = X id + Vid

(15)

The first part of the equation (14) is the former speed of the particle; the second part is “cognition”, express the think of the particle; the third part is “social”, express the information share and the cooperation between the particle [21]. “Cognition” part is explained by “law of effect” of Thorndike[22]. It is a fortified random action will possibly appear in future. The action here is “cognition”, and we suppose that getting correct knowledge is enhanced, this model supposes that the particle is inspirited to reduce deviation. “Social” part is explained by vicarious fortified of Bandura[23]. According to the anticipation of this theory, when the observer observe a model intensifying an action that will increase the probability of this action coming, that means the particle ‘s cognition will be imitated by other particle. According to equation (14) and (15) to iterate, finally obtain the optimum solution of the problem. 3.2.2.1 Coding Integer coding is adopted according to the characteristic of the problem as show in section 3.2.1.1. For describing the problem easily, we change the coding into string, the unit of solution is denoted as: X = ( x1 , x2 ,......, xm ) , here m is the length of the string, m=n(n+1)/2; every element xi in vector X correspond to the element of the upper triangular matrix of K, x1 = K11 ,

x2 = K12 , x3 = K 22 , …. xi = K hl , xm = K nn (h ≤ l ) ., xi ∈ Vi , Vi = ( xi1 , xi 2 ," xik ) = ⎣⎡1, Khlu + 1⎦⎤ . Vi is the space of the i th bit of gene, ki denote the size of this space. 3.2.2.2 Fitness Function Due to the minimizing property of the objective function, fitness function is defined as the one in section 3.2.1.2. 3.2.1.3 Random-PSO algorithm design Classical PSO algorithm is an effective method for searching continuous function extreme, but the research in discrete field is few. In 1997, Kennedy, Eberhart proposed “a discrete

13


binary version of the particle swarm algorithm”, namely PSO-SV algorithm, it used to solve binary space optimal problems, that first start to utilize PSO to solve the discrete problems [21,24]. This method can only solve the binary space optimal problems, though the performance of the algorithm is excellent, its application area is restricted, for manydimensions discrete space optimal problems, it is nail-biting. Take the problem’s particularity of account, here adopt a Random-PSO algorithm[25], and use it to solve the combinatorial optimization of actual circulating cards, which is the fixing of the circulating card number in every node enterprise of supply chain inventory control strategy. The standby card number’s range of every unit of the solution constitute the local search space, the global search space is consisted by K iju +1 local search space ( K iju is the digit capacity of the solution), that is all the standby card number’s range constitute global search space. The structure of solution of the problem is shown in figure 4[25].

Fig. 4. Structural diagram of solution space Every particle denotes the whole solution of the problem. The solution is consisted of three levels, first level is particle level, second level is every unit of solution, third level is card number; the card number of every unit constitute a local search space, the particle firstly search in the local space, choose a card number for every unit, then the card number at all unit constitute a solution. It is easy to see that the card number at all units constitute the global search space. The speed and position of the particle update as follow[25]: •

•

•

Vid = r *Vid + r1 *( Pid − X id ) + r2 *( Pgd − X id ) j

j

j

⎧ • ⎪r ∗Vid j + ( pid − X id j ) + ( pgd − X id j ) ⎪ • ⎪⎪r ∗Vid j + ( pid − X id j ) =⎨ • ⎪r ∗Vid + ( pgd − X id ) j j ⎪ ⎪ • ⎪⎩r ∗Vid j

j

r1 > 0.5, r2 > 0.5 r1 > 0.5, r2 ≤ 0.5 r1 ≤ 0.5, r2 > 0.5 r1 ≤ 0.5, r2 ≤ 0.5

(16)

14


X id j

⎧0 ⎪ ⎪ = ⎨Jd ⎪ ⎪ X id j + Vid j ⎩

X id j < 0 X id > J d 0 ≤ X id j ≤ J d

⎧numid + 1 j ⎪ numid j = ⎨ ⎪⎩numid j Fid j =

(17)

j

X id j = j

(18)

X id j ≠ j

numid j

(19)

Jd +1

The normalization of F : id j Jd

sumid = ∑ Fid j

(20)

j =0

Pid j =

Fid j

(21)

sumid

To generate a random number in 0-1 for every unit of every particle, denote as rand,

X id = arg( Pid j < rand ≤ Pid j+1 )

(22)

j

•

X id is the code of the circulating card chosen by the dth unit of the ith particle. Here“ ∗ ”is different from the normal product, it is a binocular operator, two parts of their operands can not reverse, the former part is a random number control the effect of the other one which is a

integer:

Vid ∈ [ −2 J d , 2 J d ] , Pid Pgd X id j ∈ {0,...J d } ; r ∈ ( 0,1) is inertia factor, used to

adjust the speed, r , r ∈ ( 0,1) are random numbers, used to adjust the extreme of particle 1 2 and the global extreme.;

numid j note the times of card number which is j at the dth unit of

the ith particle, P is the frequency of card number which is j at the dth unit of the ith id j particle, the probability is bigger as this value for the card number being j. 3.2.1.4 The Procedure for Random-PSO The main procedure for Random-PSO is as follows[25]: Step1: NC ← 0 (NC is iterative number)

15


To produce a random number j from every unit of particle i, j ∈ {0,...J i } ，

X id j =j

constitute the initial position of the particle, equation (19)-(20) produce the initial solution, assign this value to particle extreme, take the better one as the global extreme; Let the initial speed v=0; Step 2: if get the maximal iterative number, go to step 7, else go to step 3; Step 3: use the control matrix K as the parameter to call the simulation, obtain three economic indexes, and calculate the fitness value. Step 4: compare the currently particle fitness value and the particle extreme for every particle i, if the currently particle fitness value is better, then update Pid; Step 5: compare the currently particle fitness value and the global extreme, if the currently particle fitness value is better, then update Pgd ; Step 6: update the

Vid j and X id j follow equation (16) and (17), and produce a new solution

from equation (18)-(22), go to step 2; Step 7: output the optimal objective function value and the card combination. 3.2.1.5 Numerical Analysis In order to test the efficiency of the random-PSO algorithm, two problems in section 3.2.1.5 is used here. The comparison of the two problems is shown in table 6 taking reasonable parameters of NP and NG obtained by simulation. Problem scale

10

18

10

40

NP

NC

Best fitness

Best rate

T(s)

(4 nodes)

150

100

892611.49

0.94

13

(6 nodes)

150

150

890379.75

0.90

54

Table 6. Comparison of the results of different scale of problems for random_PSO Table 6 showed that, after the scale of the 6-node problem has expanded by 1022 times as compared with a 4-node problem, the CPU time is increased by 41s and the optimization percentage is decreased by 4%. Though there is the expanded complexity of the problem, the algorithm still possesses a better optimization percentage. The increase in the CPU time is within an acceptable range. Moreover, time increase is mainly caused by the influences of the expansion of scale of the problem on simulation. Thus, conclusively speaking, the optimal performance of the algorithm is not greatly affected by the scale increase of the problem. 3.3.3 Pheromone evolutionary algorithm This section shows the solutions of the above model for single objective optimal control strategy problem by PEA. Huang et al.[26] propose the evolutionary algorithm based on pheromone. It is a process of probability choices by making use of pheromone, which is one of the important concepts in the algorithm of ant system[27]. In the ant system, pheromone is evenly distributed in the solution space, and through the positive feedback of pheromone, the algorithm is converged to the optimal point of the whole set. In the discrete problem of evolutionary algorithm, the mutation operators every time undergo only one or several emergence in gene positions.

16


These mutation operators are lack of direction and the mutation is not even. Therefore the ability for searching optimization is weak. If pheromone is induced into the mutation operators in every mutation, the mutation operators will be more directed by making use of the changes in pheromone, which is strongly related to the fitness value. These changing pheromone guides the mutation of each gene position. Thus the mutation operators raise greatly their abilities for searching optimization. With the help of the mutation operators, the algorithm can steadily converged to the optimal of the whole search space. The evolutionary algorithm combines the concept of pheromone and the mutation operators of genetic algorithm. The probability field represents pheromone. A series of code that represents the solution of the problem is used to represent the gene series. Same probability is given to each bit of the gene in the series for initiation. Then another series is created randomly from these probabilities and their fitness values are calculated. These fitness values help to adjust the distribution of chosen probabilities. The above process is repeated until the probability distribution is steady. The series that combines the possible values of the greatest chosen probabilities is the final solution. Here, through adjustment of the probability field, each gene position of the gene series undergoes mutation and each gene can inherit their fathers’ characteristics. Under the operation of directional mutations, the algorithm is converged finally. 3.3.3.1 Coding Integer coding is adopted according to the characteristic of the problem as show in section 3.2.1.1. Following this coding rule, each solution unit has different upper bound of card number according to the numbers of nodes it has. For the sake of convenience, the above coding is changed into serial coding. Each solution unit can be described as: X = ( x1 , x2 ,......, xm ) where m is the length of the series and m = n (n + 1)/2. Each element

xi

of vector X corresponds to the element of the upper triangular part of matrix K, that is,

x1 = Knn , x2 = K12 , x3 = K22 ,", xi = Khl , xm = Knn (h ≤ l) xi ∈Vi ,Vi = ( xi1 , xi 2 ,...xiki ) = ⎡⎣1, K hlu + 1⎤⎦ . Vi is the space of the ith bit of gene, ki denote the

size of this space. Vi corresponds to a probability distribution. Pi = ( pi1 , pi 2 ,......, pik ) where pij corresponds to i

xij and

ki

∑p j =1

ij

= 1 . Pi is the probability field of Vi and P = {Pi } .

3.3.2.2 Fitness function Due to the minimizing property of the objective function, fitness function is defined as the one in section 3.2.1.2. 3.3.3.3 Generating new generation The value of X is formed according to the probability field and xi is chosen by roulette method[26]. If

l −1

∑P j =1

ij

l

< rand [0,1] ≤ ∑ Pij , then xi = xil j =1

(23)

17

Optimal Control Strategy for Serial Supply Chain j

Where Pij = ∑ pih , Pi 0 = 0 . The values of h =1

xi (i = 1, 2,3," , m) are obtained by the above

method. Thus X is formed. 3.3.3.4 Adjustment of the probability field In order to let the probability field converge, continuous adjustment is needed. Assume that the fitness value of a certain solution unit X old of the last generation is fitnessold = F ( X old ) where F ( X ) is a function of fitness value that is determined by the practical problem. When the fitness value of the unit X new of the preceding generation fitnessnew = F ( X new ) , the probability field P of xij is adjusted as below: The probabilities pij that correspond to xij are changed into pij ⋅ (1 + Δpij ) . Then pi1 , pi 2 … pik . i

undergo the unified process and

i = 1, 2,3," , m .Here,

⎛ fitnessnew ⎞ (24) Δp ij = α ⋅ arctan⎜⎜ − 1⎟⎟ ⎝ fitness old ⎠ Δpij is the amount for probability adjustment. α is the adjustment coefficient. The choices of adjustment coefficient of the probability field and the stoppage parameters are important and delicate. If the adjustment coefficient of the field is too big, then the speed of convergence is too fast, it will easily give the local optimization. If the coefficient is too small, then the speed is too slow, and the efficiency of the algorithm becomes low. That is why the substantial adjustment has to be done according to the practical problem of the instance simulation. The continuous adjustment of P increases the probability of getting solution units with high fitness values. At the same time, through unified process, the probability of having solution units with low fitness values decreases. This situation gives a very high probability of getting the optimal solution. The function in probability adjustment is a tangent function, arctan ( x ) . It prevents the value of Δpij being too large to find the global optimal solution. Actually, in the adjustment process, the fitness values are the directed values that operate mutation in the probability field. Mutations happen at each gene position of every generation simultaneously. Therefore, the genes have learned their fathers’ characteristics thoroughly and this hastens the algorithm’s speed of convergence. 3.3.3.5 Initialization In order to guarantee the algorithm searches within global space, at the initiation stage, each solution should have the same probability of being chosen. That is pij which should have even distribution.

pij = 1 , I = 1, 2,3," , m; j = 1, 2,3," ki ki 3.3.3.6 The termination criterion The largest generation is chosen as the termination criterion. 3.3.3.7 The Procedure for PEA The main steps in the flow of the solution are:

(25)

18


Step 1: NC ← 0 (NC are the iterative steps) The algorithm based on pheromone evolution produces the population X of the initial solutions and their corresponding initial probability field P. The actual procedure is to create a random number q for each xij of every solution unit xi. q∈Vi and xij = qforms the initial solution X which becomes the input for the simulation. The probability field is initialized according to formula (25); Step 2: The fitness values fitnessold=F(Xold) of each solution of the last generation Xold are calculated; then turn to step 3; Step 3: If the termination criterion is satisfied, turn to step 6; or else, create a solution of the new generation Xnew and process it in the simulation. Then the fitness values fitnessnew=F(Xnew) of each solution of the present generation are calculated; Step 4: The new probability field that corresponds to Xnew is obtained by adjusting the probability field according to formula (24); Step 5: Let Xold ← Xnew, then turn to step 2; Step 6: The optimal value of the objective function and the corresponding circulating card number are output. 3.3.3.8 Numerical Analysis In order to test the efficiency of the PEA algorithm, two problems in section 3.2.1.5 is used here. The comparison of the two problems is shown in table 7 taking reasonable parameters of NP, NG, PC, PM obtained by simulation. Problem scale 1018 (4 nodes) 1040 (6 nodes)

NP 200 200

NG 150 150

Best fitness 892611.49 890379.75

Best rate 0.92 0.88

T(s) 19 41

Table 7. Comparison of the results of different scale of problems for PEA Table 7 has shown that, after the scale of the 6-node problem has been expanded by 1022 times as compared with a 4-node problem, the CPU time is increased by 22s and the optimization rate is decreased by 4%. Though there is the expanded complexity of the problem, the algorithm still possesses a better optimization rate and the increase in the CPU time is within an acceptable range. Moreover, time increase is mainly caused by the influences of the expansion of scale of the problem on simulation. Thus, conclusively speaking, the optimal performance of the algorithm is not greatly affected and it is still a fairly stable algorithm. 3.3.4 The comparison among the different algorithms Finally the GA, Random-PSO and PEA is compared. The results are shown in Table 8. Algorithm (problem scale) GA ( 1018 ) Random-PSO ( 1018 ) PEA ( 1018 ) GA ( 1040 ) Random-PSO ( 1040 ) PEA ( 1040 )

NP 200 150 200 200 150 200

NG 150 100 150 150 150 150

Best rate 0.92 0.94 0.92 0.86 0.90 0.88

T（s） 21 13 19 39 54 41

Table 8. Comparison among GA, Random-PSO and PEA for different scale of problems


19

Table 8 has shown that, the Random_PSO has highest optimization rate when the scale of problem increase, and the CPU time of the three algorithms is similar. Therefore, the Random-PSO is more effective than GA and PEA for this kind of problem. 3.4 Conclusions Determination of the optimal control strategy is a key factor for a successful supply chain. This chapter has made researches on the optimal inventory control strategy of a serial supply chain and presents the description of a two-level model. The first-level model is a nonlinear integer-programming model. Its main purpose is to determine the optimal control strategy which gives the minimal overall cost of a supply chain under some constraints. These constraints include the customer service no less than the given value and the standard deviation of input less than a given value. When the inventory control strategy is given, the second-level model is used to obtain the performance measurements of the supply chain. The first-level model reaches optimization through the algorithm based on intelligent algorithm. The intelligent algorithms of GA, random-PSO and PEA is considered in this study. The second-level model implements the general push/pull model of inventory of a serial supply chain by simulation. The main characteristic of the second-level model is that, the choice of control, push or pull, of a node is determined by whether it is under the feedback control of its downstream nodes. This has the potential to be an efficient quantitative tool for more complex SC analysis in the global business environment. Instances of different scales of problems are analyzed. The results shown the effectiveness and the efficiency of the method. The cost of the whole supply chain is minimized while satisfying the customer demands and limiting the “bullwhip effect”. It balances production rhythm and shared benefits of each node enterprise of a supply chain, which gives a quantitative support of rational organization of purchase, production, transportation, and sales. Finally, comparison study of different scales of problem for three intelligent algorithms is given. Results shows that the random_PSO has highest optimization rate when the scale of problem increase, and the CPU time of the three algorithms is similar. It suggested that the random-PSO is more effective than the other two algorithm for this kind of problem. This work was supported by the National Natural Science Foundation of China (Grant no. 70671020, 70721001, 70431003, 60673159), the Program for New Century Excellent Talents in University (Grant no. NCET-05-0295, NCET-05-0289), Specialized Research Fund for the Doctoral Program of Higher Education (Grant no. 20070145017, 20060145012), the National High-Tech Research and Development Plan of China (Grant No. 2006AA01Z214), the Key Project of Chinese Ministry of Education (Grant No. 108040), the Natural Science Foundation of Liaoning Province (Grant No. 20062022).

4. Reference Chopra S , Meindl P, Supply Chain Management (3rd Edition) , Prentice Hall, 2006 Hugos M, Essentials of Supply Chain Management (2nd Edition), Wiley , 2006. Dolgui A, Soldek J, Zaikin O, Supply chain optimization: product/process design, facility location and flow control, New York: Springer, 2005. Gstettner S, Kuhn H, Analysis of Production Control Systems Kanban and CONWIP. International Journal of Production Research, 1996, 34(11): 3253-3273. Deleersnyder J. L., Hodgson T. J., Mueller H, et al. Kanban Control Led Pull System: Analysis Approach. Management Science, 1989, 35(9): 1079-1091.

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Sarker B. R., Fitzsimmons J. A., The Performance of Push and Pull Systems: A Simulation and Comparative Study. International Journal of Production Research, 1989, 27(11): 1715-1732. Flapper S. D. P., Miltenburg G. J.,Wijugard J., Embedding JIT into MRP. International Journal of Production Research, 1991, 29(2): 329-341 Larsen N. E. Alting L. Criteria for selecting production control philosophy. Production Planning & Control, 1993, 4(1): 54-68 Villa A., Watanabe T. Production management: Beyond the Dichotomy Between “Push” and “Pull”. Computer Integrated Manufacturing Systems, 1993, 6(1): 53-63 Spearman, M. L., Woodruff, D. L., and Hopp, W. J. CONWIP: a Pull Alternative to Kanban. International Journal of Production Research, 1990, 28(5): 879-894. Gaury E, Kleijnen JPC and Pierreval H, A methodology to customize pull control systems, Journal of the Operational Research Society, 2001, 52, 789–799. Ovalle O R., Marquez A C., Exploring the Utilization of a CONWIP System for Supply Chain Management. International Journal of Production Economics,2003,83:195-215 Herer Y. T., Masin M. Mathematical Programming Formulation of CONW IP Based Production Lines and Relationships to MRP. International Journal of Production Research, 1997, 35 (4): 1067-1076. Bose, G.J., Rao, A. Implementing JIT with MRP Creates Hybrid Manufacturing Environment. Industrial Engineering. 1988, 20(9): 49-53. Michalewicz, M, Genetic algorithms + data structure = evolution programs. (3rd rev. and extended ed). Berlin; Hong Kong: Springer, 1996 Mitchell, M. An introduction to genetic algorithms. The MIT Press, Cambridge, Massachusetts, London, England, 1996. Kennedy J , Eberhart R. Particle swarm optimization. Proc IEEE International Conference on Neural Networks, Perth, Australia 1995,1942-1948. Eberhart R C, Kennedy J A. A new optimizer using particle swarm theory. Proc 6th Int. Symposium on Micro Machine and Human Science. Nagoya, 1995. 39-43. Wilson E O. Sociobiology : The New Synthesis. .MA: Belknap Press, 1975. Shi Y., Eberhart R. A modified particle swarm optimizer. Proc IEEE Int. Conf Evolutionary Computation. Anchorage, 1998. 69-73. Kennedy J. The particle swarm: Social adaptation of knowledge. P roc IEEE Int. Conf on Evolutionary Computation. Indiam apolis, 1997. 303-308. Thorndike E L. Animal Intelligence: Empirical Studies. New York: MacMillan, 1911. Bandura A .Social Foundations of Thought and Action :A Social Cognitive Theory. New Jersey: Prentice 2 Hall, 1986. Kennedy J. Eberhart R.A Discrete binary version of the particle swarm algorithm. In: Proceedings of the IEEE International Conference on Systems. Man and Cybernatics, 1997, 5:4104-4108. Huang M, Wu X J, Wang X W, Ip W X, Yung K L, The risk programming of virtual enterprises based on algorithms of Min / Max / Mean / Random-PSO, 1st IEEE Conference on Industrial Electronics and Applications (ICIEA 2006), 2006.05, Singapore, 997-100 Huang Z. C., Wu F. C., Hu X. L., An evolutionary algorithm to integer programming problem based on pheromone, Reaserch on Computer Applying, 2001, 7(2), 27-29. Dorigo M, Maniezzo V. A colony ant system: optimization by a colony of cooperating agent, IEEE Tran. On System, Man and Cybemetics Part B,1996, 26(1),34-37.

2 A Synergistic Approach towards Autonomic Event Management in Supply Chains Roy Oberhauser

Aalen University Germany

1. Introduction Supply Chains (SCs), due to their very nature and intent (e.g., embracing change in markets, products, manufacturing, partners, globalization) in conjunction with market pressures, will face ongoing challenges that are necessarily reflected on the Information Technology (IT) infrastructure used to manage and optimise their operations. Supply Chain Management Software (SCMS) typically covers the various functional aspects of SCs, including integration technology. The result of the IT integrations is a form of an information supply chain, including computational representations of physical SC entities. For purposes of this chapter SCMS will be considered to incorporate any ERP solutions and/or IT infrastructure utilized to enable the information integration required to support the SCs. Current SC IT challenges include decision making, collaboration, and attaining qualities such as scalability, performance, integratability, correctness, and reliability in the face of the perpetual dynamics and increasing complexity of SCs. To avoid disruptions to SCs, Supply Chain Event Management (SCEM) considers the set of possible event scenarios and plans solutions. Events can be either representations of realworld events or can be introduced as a side-effect of the Information Systems (IS) supporting the SC (IT events). SCs can achieve their goals for optimal management of operations only to the extent and degree that they manage and automate the necessary information flow, especially with regard to managing unexpected events. The effective handling of potentially disruptive events is vital to achieving the aforementioned qualities, yet the ongoing change (mirrored in the IT systems) in entities and the properties and relations thereof, necessarily limits the sufficiency and totality of predefined solutions. A synergistic approach that leverages various computing paradigms can provide improved SCEM solutions. In the face of potentially disruptive SC and IT events (referred to as SCEs in this chapter), autonomic computing (AC), inspired by the human autonomic nervous system, with its stated goals of self-configuration, self-optimization, self-healing, and self-protection (also known as self-X), would appear to be a synergistic candidate for improving SCEM. While some properties defined for autonomic systems1 may not be applicable to SCEM, others will be beneficial. A partial application of AC techniques to achieve improved reactive event

1

http://www.research.ibm.com/autonomic/overview/elements.html

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management might be both practical and beneficial to SCEM. However, the changeability, heterogeneity, distribution, internationalization/localization, support, governance issues, and partner interdependencies in SCMS (both from an IT and linguistic/cultural viewpoint) makes SCEM and self-X attainment in SC and SCMS far more challenging compared to that of a self-contained rigid system. Granular Computing (GC) is a paradigm that concerns itself with the processing of complex information entities called information granules, recognizing that at different abstraction levels of data, different relationships can be inferred (Pedrycz, 2001), (Bargiela et al., 2003), (Pedrycz et al., 2008). The meaning and impact of an SCE is also dependent on the granularity at which it is viewed, and other implications and trends may be detected at various abstraction levels. To enable internationalization and decoupled SC partner agents to autonomically collaborate to address SCEs, it is imperative that the meaning for shared concepts be defined. The Semantic Web (SemWeb) adds machine-processable semantics to data (Berners-Lee et al., 2001). SemWeb computing (SWC) allows for greater and improved automation and integration of information in large information SCs due to its formal structuring of information, clearly defined meanings and properties of domain concepts, and standardized exchange formats. One of the issues facing SemWeb is the creation and adoption of standardized ontologies in OWL (Web Ontology Language) (McGuinness et al., 2004) for the various industry domains to precisely define the semantic meaning of the domain data – standardization is laborious and adoption is slow. However, to address both the challenge of SCEM to avoid disruptive impacts and the challenge of SCMS to achieve self-X and other qualities in a heterogeneous, changing, loosely-coupled and global environment, a transitional hybrid stage is proposed. A high-value event-specific subset tailored to the SCMS is tackled first that enables the collaborative involvement of partner agents (computing or human). In other words, if the partners have no agreement on a common meaning of an event, the concepts necessary to diagnose the indicative problem, and the meanings of the actions required in a solution, then the required collaborative and (partially to completely) automatable solutions for interdependent and non-trivial situations will continue to be elusive. Additionally, to enable collaboration, partner exchangeability, and sharing across heterogeneous IT partner services and data, standardized access protocols for SCMS and SCEM is desirable if not essential. Service-oriented Computing (SOC), with its reliance on Web Services (WS), provides platform-neutral integration for arbitrary applications (Alonso et al., 2003). Furthermore, Space-Based Computing (SBC) is a powerful paradigm for coordinating autonomous processes by accessing a distributed shared memory (called a tuple space) via messaging, thereby exhibiting linear scalability properties by minimizing shared resources. Tuple spaces implement a shared data repository of tuples (an ordered set of typed fields) that can be accessed concurrently in a loosely-coupled way based on the associative memory paradigm for parallel and distributed computing first presented by (Gelernter, 1985). This chapter explores the potential for SCs that a synergistic approach to SCEM (SASCEM) that leverages various computing paradigms provides for improving the qualities of SCEM, especially with regard to approaching self-X properties and automation. The rest of the chapter is organized as follows: Section 2 presents a review of the literature. In Section 3 the solution approach is presented. Section 4 presents initial implementation

A Synergistic Approach towards Autonomic Event Management in Supply Chains

23

work based on the solution approach. In Section 5 preliminary results which evaluated certain performance and scalability characteristics are discussed, followed by a conclusion.

2. Literature review (Mischra et al., 2003) describes an agent-based decision support system for a refinery SC, where agents collaborate to create a holistic strategy using heuristic rules. (Bansal et al., 2005) present a model-based framework for disruption management in SCs, generalizing the approach of (Mischra et al., 2003). Related to SCs, Value-Added Networks (VANs) are hosted service offerings that add value to common networks by acting as an intermediary between business partners for sharing proprietary or standards-based data via shared business processes. As such they can be viewed as supporting informational SCs. Work on modelling collaborative decision making in VANs includes MOFIS (Naciri et al., 2008) and could be applied to improving SCEM, e.g., via integration of the concepts in a SASCEM. Complex Event Processing (CEP) (Luckham, 2002) is a concept to deal with meaningful event detection and processing using pattern detection, event correlation, and other techniques to detect complex events from simpler events. Besides the research work that considers various aspects of CEP (e.g., high volume, continuous queries), commercial products include the TIBCO Complex Event Processing Suite. The Resource Event Agent (REA) model aims at providing a basic generic shared data model that can describe economic phenomena of several different systems, both within and between enterprises of many different types (McCarthy, 1982). Work includes (Haugen et al., 2000) who present a semantic model for SC collaboration, (Hessellund, 2006) discusses SC modelling extensions to REA, while (Jaquet et al., 2007) presents a semantic framework for an event-driven operationalization and extension of the REA model that preserves flexibility and heterogeneity. An extended REA approach and hybrid/partial semantic formalization of events are congruent with a SASCEM. Multi-Agent Systems (MAS) have been researched extensively, as has MAS in combination with SCs. Agent-based event management approaches includes Sense, Think & Act (ST&A), which exhibits function-driven, goal-driven (local goals), and collaborative goal-driven (global goals) behaviours (Forget et al, 2006). Agent-oriented supply-chain management is explored in (Fox et al., 2000) among others. (Adla, 2008) proposes an integrated deliberative and reactive architecture for SCM for supporting group decision making. Although this work has typically not utilized SOC and SWC, enabling and leveraging the integration of such problem-solving approaches is one goal of a SASCEM. Work on semantic enhancement of tuple spaces includes sTuples (Khushraj et al., 2004), which extends the object-oriented JavaSpace implementation (Freeman et al., 1999) with an object field of type DAML-OIL Individual. (Tolksdorf et al., 2005) and (Tolksdorf et al., 2005a) describe work on Semantic Tuple Spaces. The Triple Space Computing (TSC) project2 aims to develop a communication and coordination framework for the WSMX Semantic Web Service platform (Bussler et al., 2005) (Simperl, 2007). However, there has been insufficient exploration of the application of semantically-enhanced tuple spaces for collaborative event-based problem solving in general, and for SCEM in particular.

2

http://tsc.deri.at

24


With regard to partner communication interoperability, the issue of scalable server-side push notification protocol over HTTP for Space-based Computing (SBC) is explored in (Kahn et al., 2007) but lacks standardization. Agent-interoperability via Web Services has been explored, e.g., JADE WSIG (Greenwood, 2005), but its application to SCs is still hampered due to a lack of standardization, e.g., by FIFA (Greenwood et al., 2007).

3. Solution To achieve improved and more holistic solutions for SCEs while exhibiting AC and other expected qualities, the SASCEM is a synthesis of various areas of computing, specifically granular (GC), semantic web (SWC), service-oriented (SOC), space-based (SBC), event-based (EBC), context-aware (CAC), multi-agent (MAC), and autonomic computing (AC) as shown in (Fig. 1).

Fig. 1. Synergistic Solution Approach to SCEM Solution constraints include heterogeneity, e.g., in partner agent implementations, rulebased techniques, platform software, and the adaptive and dynamic specialization of problem-solving for SCs. Additionally, it is assumed that for non-trivial SCs, no complete autonomic problem-solving for SCEM is as yet practical, thus the involvement of humans to the necessary degree is subsumed. Principles that guided the solution approach include shared-nothing, decentralization, loose-coupling, standards-based communication, exchangeability (e.g, of collaborative decision making agent techniques), and enabling hybrid subsets for practical collaborative problem solving in SCs. A simplified distributed SC solution infrastructure is shown in (Fig. 2). Using the SBC paradigm, tuple spaces are used to store event and event-relevant data, without deciding on meaning. Separate Semantic Web-aware tuple spaces are then used for collaboration on event diagnosis, problem prescription, and prognosis. Proactions or reactions are then initiated by partner agents and may involve the invocation of Partner or Infrastructure Services. Infrastructure Services and Partner Services provide the integration and access to SC (partner) functionality in accordance with the SOC paradigm. Heterogeneous interoperability and accessibility is supported via standards-based Web Services protocols, such as SOAP and REST (zur Muehlen et al., 2005). While an Enterprise Service Bus (ESB) is


25

possible, its use depends on the SCMS and SCEM needs. In place of WS, Semantic Web Services (SWS), which envisions enabling automatic and dynamic interaction between software systems (Studer et al., 2007), might be a consideration; however, since the data repository can be readily accessed using simpler WS interfaces, a pragmatic approach utilizing the minimal amount of SemWeb to the extent needed to enable partner collaboration is currently preferable until SWS maturity and adoption has progressed.

Fig. 2. Solution Infrastructure of the SASCEM (simplified) The details of the solution approach will follow the event process steps shown in (Fig. 3).

Fig. 3. Event Process Steps in the SASCEM 3.1 Event acquisition The acquisition of SCEs can come from sensors, partner machines and IT systems or services, and other event producers. In accord with EBC, the functionality of SCEM is triggered and invoked in response to the generation of SCEs. The events can be simple events to complex events inferred from simpler events, as considered in CEP. To enable the advantages of GC, these SCEs should be retained in their original state and supplementary complex events generated when these are detected via pattern matching or other CEP techniques by partner agents or other components. CEP and GC can be incorporated in (Partner or Infrastructure) Services or Agents. 3.2 Event storage The event data is stored as a tuple in a tuple space following the SBC paradigm. This allows decoupled partner agents to flexibly subscribe to and be notified of relevant events. The tuple can be retrieved over time by various partner agents. The data model is a hybrid that keeps data-only SCE tuples separate from the SemWeb tuple space. The SASCEM uses a hybrid transitional approach of communication between agents, supporting a blend of SemWeb and other data exchange in the tuple spaces. This allows the original event data to be viewed at different times, at different granularity levels, and to have multiple and even contradictory interpretations by diverse partner agents. Registration for notifications by partner agents can be based on event data arrival, event data changes, etc., independent of semantic events. Thus partner agents without semantic

26


awareness but, e.g., with viable event handling rules and heuristics, can participate and support SCEM. Those partner agents with SemWeb capabilities can collaborate in the SemWeb tuple space and create and adjust the semantic meaning of the event data, type, attributes, and relations at different levels of abstraction and perhaps in different ontologies. This includes analysis and processing with regard to the event’s relation to a problem (if any), diagnosis, prognosis, prescription, actions, etc. necessary to resolve it. 3.3 Contextual annotation Contextual annotation of the event supports the retrieval of relevant data close to the occurrence of the event, and helps to determine its meaning and implications as well as infer complex events. As events are diagnosed over time, it may be determined by partner agents that certain information which is applicable and relevant should be gathered and other information may be determined to be irrelevant. CAC is thus utilized to annotate contextual and environmental information with the event, and those services registered for the event are notified. If no RDF(S)3 (Brickley et al., 2004) information is provided with the event, then this too could be annotated to provide a uniform way of describing information resources associated with the event. 3.4 Event diagnosis The correct diagnosis of SCEs is dependent on appropriate knowledge and rules, and due to the partner interdependency of SCs, collaborative effort to achieve AC is necessary. Diagnostic MAC enables the various partner agents to specialize in their particular knowledge without the limitations that a centralized single agent would incur. In order for heterogeneous partner agents to collaborate to achieve (semi-)autonomic behaviour, SWC is utilized to allow for a standardized and extensible approach for giving meaning to the events. A SemWeb-enabled tuple space (SWETS) provides a shared data storage where the meaning of the data types is defined and collaborative event analysis and interpretation is thus enabled. SemWeb-aware agents using inference engines can collaborate at various abstraction levels using GC paradigms. Complex events can be inferred from simple events, e.g., regarding their timing, sequence, patterns, or trends, and CEP could be utilized. If the collaborative diagnosis relates the event to a(n) (unknown) problem, processing continues, otherwise it is completed. Multiple and even contradicting diagnoses are allowed and may occur. Note that this situation may in turn create a new event which in turn goes through the processing steps. Ontologies are minimally necessary for the intersection set of concepts necessary for SCEM between partner agents. In this regard, full ontologies that cover all possible concepts in the SC can - but must not necessarily, be avoided. A partial application of SemWeb appears practical and reasonable at this time, given some current practical limitations with regard to payoff vs. effort, standardization, maturity, industrial usage, training, tooling, etc. Yet the intersection of concepts between partners requires a formal definition and agreement in order for collaborative and automated SCEN to be enabled.

3

Resource Description Framework (Schema)


27

While agents are often considered to be artificial computational entities that perform tasks with a degree of autonomy, in the SASCEM agents include the set of human agents as well for problem solving, supporting a hybrid spectrum from completely manual to automatable diagnosis and solutions, since each SC is unique and for non-trivial dynamic SCs new events and problems may occur that require human intervention before they become automatable. 3.5 Problem prescription Using the SWETS, the agents, based on the possible diagnoses, collaboratively decide on a prescription consisting of a set of actions, e.g. using (Adla, 2008) or other decision techniques, and incorporating AC techniques where applicable. 3.6 Problem prognosis Separately from the prescription, the forecasted impact, side-effects, and success chances of the diagnosis and/or the prescription in the form of a prognosis could optionally be (collaboratively) determined and placed in the SWETS, perhaps triggering new events. 3.7 Proactions and reactions Based on the prescription and/or prognosis, the reactions are executed by the appropriate agent(s), using partner or infrastructure services as needed, and preventative proactions can be executed to limit the impact of side effects, repeated problems, etc.

4. Solution implementation The prototype implementation of the SASCEM currently includes an adaptation of an open source tuple space implementation (XSpace4). Hybrid support for SWETS is currently dependent on the outcome of a tsc++5 evaluation and integration. Apache Axis26, which supports asynchronous WS, was used for WS communication. To illustrate the SASCEM implementation and for prototype testing purposes, an ontology (Fig. 4) for a software SC was created using Protege 3.3.1. First it will be described in prose, followed by OWL abstract syntax. Work on SCM ontologies includes (Haller et al., 2008). BusinessObjects can depend on other BusinessObjects and have Suppliers, Consumers, and Producers. A Service is a BusinessObject with a Protocol, including human and organizational services, and can be specialized as a WS or a SWS. Products and Information are Artifacts, which are BusinessObjects. Systems, Hardware, and Software are Products and Products may have a Configuration. A Patch is Software. A Document is Information. Events may refer to one or more BusinessObjects and be associated with one or more Problems. Problems refer to a Quality that is affected, may include a Diagnosis and a Prognosis. A Diagnosis may include a Prescription that may refer to a set of Actions and may refer to a Patch and/or Configuration.

http://xspacedb.sourceforge.net/ http://tsc.sti2.at/ 6 http://ws.apache.org/axis2/ 4 5

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Fig. 4. Partial Software Supply Chain Event Management Domain Ontology An alphabetical listing in OWL abstract syntax follows (Listing 1): Class(Action partial owl:Thing) Class(Artifact partial BusinessObject) Class(BusinessObject partial restriction(hasEvent minCardinality(0)) owl:Thing restriction(version cardinality(1)) restriction(name cardinality(1)) restriction(depends minCardinality(0)) restriction(hasConsumer minCardinality(0)) restriction(hasProducer minCardinality(1)) restriction(hasSupplier minCardinality(0))) Class(Configuration partial owl:Thing) Class(Consumer partial restriction(hasBusinessObject minCardinality(1)) owl:Thing restriction(name cardinality(1)) restriction(homepage cardinality(1)))


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Class(Diagnosis partial restriction(hasPrescription minCardinality(0)) owl:Thing) Class(Document partial Information) Class(Event partial restriction(hasProblem minCardinality(0)) restriction(hasBusinessObject minCardinality(0)) owl:Thing) Class(Format partial owl:Thing) Class(Hardware partial Product) Class(Information partial Artifact restriction(hasFormat cardinality(1))) Class(Patch partial Software) Class(Prescription partial restriction(hasPatch maxCardinality(1)) restriction(hasAction minCardinality(0)) restriction(description cardinality(1)) owl:Thing) Class(Problem partial restriction(hasEvent minCardinality(0)) owl:Thing restriction(description cardinality(1)) restriction(hasImpact minCardinality(0)) restriction(hasSolution minCardinality(0)) restriction(hasQuality minCardinality(0))) Class(Product partial restriction(hasConfiguration minCardinality(0)) Artifact) Class(Prognosis partial owl:Thing restriction(description cardinality(1))) Class(Protocol partial owl:Thing) Class(Quality complete oneOf(Functionality Reliability Usability Efficiency Maintainability Portability)) SubClassOf(Quality owl:Thing) Class(SemanticWebService partial WebService) Class(Service partial restriction(hasProtocol cardinality(1)) BusinessObject) Class(Service partial restriction(hasProtocol cardinality(1)) BusinessObject) Class(Software partial Product) Class(Supplier partial owl:Thing restriction(name cardinality(1)) restriction(homepage cardinality(1))) Class(System partial Product) Class(WebService partial Service)

Listing 1. Partial Software Supply Chain Event Management Domain Ontology

5. Results Preliminary results considered the viability of the solution architecture and prototype implementation used for this peer-based middleware combination of a tuple space, relational database, message broker, and asynchronous Web Services infrastructure for addressing the SC qualities in scalability on a per-agent and a system level before integrating true SemWeb-aware problem-solving agents. For this, two key throughput scenarios were measured consisting of the event message into the tuple space (put scenario) and the notify scenario to other agents (notify scenario).

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The test configuration consisted of 2,4 GHz Dual Core Opteron 180 PCs running Windows XP Pro SP2, 3.3GB RAM, 100 Mbit LAN, JRE 1.6.0_07, and Apache Axis2 0.93. One server PC ran Xspace 1.1, Jboss 4.0.3, and HSQLDB 1.8.0. The averages over three runs were used for all results (Fig. 5). For the notify scenario, 1000 SOAP messages containing an event to put into the tuple space were sent from a single producer PC to the server, with a Message-Driven Bean, upon receiving the put, notifying agents (via asynchronous SOAP messages) on either 1, 2, 4, or 8 consumer PCs. Note that all the throughput results exclude and ignore the server and the producer PCs, but only consider the notification throughput on the consumers. The results show that asynchronous notifications by the tuple service to 1 to 2 and 4 peers regarding the put allowed an almost linear scalability, with a reduction at 8 peers due to full CPU utilization on the server. For the put scenario, 1000 SOAP messages containing an event to put into the tuple space were sent from either 1, 2, 4, or 8 producer PCs to the same tuple space on the server PC. The results show a significant reduction in cumulative throughput with each added peer, which can be explained by the transactional bottleneck of the puts to the relational database on the server. These results and storage options, including persistence requirements on a per tuple basis, will be taken into account and optimization opportunities considered in future work.

Fig. 5. Average throughput vs. number of peers for web service notifications Since for SASCEM the number of notifications is expected to be much higher than the number of generated events, the nearly linear scalability for notifications show that the SBC and EBC foundation for SASCEM is viable for SCEM.

6. Conclusion The increasing reliance on SCs, coupled with increasing complexity, dynamism and heightened quality expectations, are necessarily reflected in the SCMS and implicitly in the need for improved SCEM to limit disruptions and achieve self-X qualitites. A novel synergistic approach to SCEM, as presented in this chapter (SASCEM), leverages the computing paradigms of granular, semantic web, service-oriented, space-based, event-based, context-


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aware, multi-agent, and autonomic computing to create a holistic solution approach that can change how SCEM is approached. Within the SASCEM, the hybrid approach to SWC makes adoption practical and viable in the near term. Preliminary results show sufficient performance and scalability qualities for such an SBC infrastructure to address SCEM. The scope of applicability for this approach goes beyond SCEM, and could be applied to event management in general outside of SCs. Moreover, SCMS might be architected differently where a SASCEM adopted. Future work includes integrating SemWeb-based problem-solving agents with Semantic Web-aware tuple spaces and evaluating the solution with regard to real-world problemsolving scenarios.

7. Acknowledgements Thanks to Tobias Gaisbauer for his assistance with the experiments and implementation.

8. References Adla, A. (2008). Agent-Based Group Decision Making, Proceedings of The Third International Conference on Software Engineering Advances (ICSEA 2008), pp.146-151, ISBN 978-07695-3372-8, Sliema, Malta, October 2008, IEEE Computer Society Alonso, G.; Casati, F.; Kuno, H. & Machiraju, V. (2003). Web Services – Concepts, Architectures and Applications, Springer Verlag, Berlin Bansal, M.; Adhitya, A.; Srinivasan, R. & Karimi, I.A. (2005). An Online Decision Support Framework for Managing Abnormal Supply Chain Events, 38th European Symposium of the Working Party on Computer Aided Process Engineering: ESCAPE-15, 29 May - 1 June 2005, Barcelona, Spain, Elsevier Science B.V., ISBN 0444519912 Bargiela, A. & Pedrycz, W. (2003). Granular Computing: An Introduction, Dordercht: Kluwer Academic Publishers Berners-Lee, T.; Hendler, J. & Lassila, O. (2001). The Semantic Web, The Scientific American, May 2001. Brickley, D. & Guha, R.V. (2004). RDF Vocabulary Description Language 1.0, RDF Schema, W3C Recommendation, February 2004, http://www.w3c.org/TR/rdf-schema/ Bussler, C.; Kilgarriff, E.; Krummenacher, R.; Martin-Recuerda, F.; Toma, I. & and Sapkota, B. (2005) WSMX Triple-Space Computing, http://wsmo.org/TR/d21/v0.1, June 2005, D21 v0.1 Forget, P. ; D’Amours, S. & Frayret, J.-M. (2006). Collaborative event mangement in supply chains: an agent-based approach, Information Technology for Balanced Manufacturing Systems, volume 220, IFIP International Federation for Information Processing, 2006, Springer, Boston, USA, pp. 89–98 Fox, M. S.; Barbuceanu M. & Teigen, R. (2000). Agent-oriented supply-chain management. International Journal of Flexible Manufacturing Systems, 12(2-3), pp. 165–188 Freeman, E.; Hupfer, S. & Arnold, K. (1999). JavaSpaces Principles, Patterns and Practice, The Jini Technology Series, Addison-Wesley, Reading MA, USA. Gelernter, D. (1985). Generative communication in Linda, ACM Transactions on Programming Languages and Systems, volume 7, number 1, January 1985, pp. 80-112 Greenwood, D. (2005). JADE Web Service Integration Gateway (WSIG), JADE Tutorial, AAMAS 2005

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Greenwood, D.; Lyell, M.; Mallya, A. & Suguri, H. (2007). The IEEE FIPA approach to integrating software agents and web services, Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems, Honolulu, Hawaii, 2007, Article No. 276, ISBN:978-81-904262-7-5 Haller A.; Gontarczyk, J. & Kotinurmi P. (2008). Towards a complete SCM ontology: the case of ontologising RosettaNet, Proceedings of the 23rd Annual ACM Symposium on Applied Computing, ACM, Fortaleza, Ceará, Brazil, 2008, pp. 1467-1473 Hessellund, A. (2006). Supply Chain Modeling with REA. TR 2006-80, 2006, The IT University of Copenhagen, Denmark Jaquet, M. & Østerbye, K. (2007). A Property Driven Approach Towards Creating an Adaptive REA Framework, Proceedings of the REA-25 Conference, June 2007, Delaware, USA Kahn, E.; Riemer, J. & Lechner, L. (2007). XVSMP/Bayeux: A Protocol for Scalable Space Based Computing in the Web, Proceedings of the 16th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2007), pp. 68-73 Khushraj, D.; Lassila, O. & Finin, T. (2004). "sTuples: Semantic Tuple Spaces," First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous'04), pp.268-277 Luckham, D. (2002). The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems, Addison-Wesley Professional, ISBN 0-201-72789-7 McCarthy, E. W. (1982). The REA Accounting Model: A Generalized Framework for Accounting Systems in a Shared Data Environment. The Accounting Review, July 1982, pp. 554-78 McGuinness, D. L. & van Harmelen, F. (2004). OWL Web Ontology Language: Overview, W3C Recommendation, February 2004, http://www.w3c.org/TR/owl-features/ Naciri, S.; Yoo, M. & Glardon, R. (2008). MOFIS: new conceptual modeling framework for handling value adding networks complexity, Proceedings of The Third International Conference on Software Engineering Advances (ICSEA 2008), pp.516-522, ISBN 978-07695-3372-8, Sliema, Malta, October 2008, IEEE Computer Society Pedrycz, W. (2001). Granular Computing: An Emerging Paradigm, Heidelberg: Physica-Verlag Pedrycz, W.; Skowron, A. & Kreinovich, V. (2008). Handbook of Granular Computing, WileyInterscience, ISBN: 978-0-470-03554-2 Simperl, E.; Krummenacher, R. & Nixon, L. (2007). A coordination model for triplespace computing, Proceedings of the 9th International Conference on Coordination Models and Languages (Coordination), Springer Verlag, June 2007 Studer, R.; Grimm, S. & Abecker, A. (2007) Semantic Web Services: Concepts, Technologies, and Applications, Springer, ISBN-10: 3540708936 Tolksdorf, R.; Bontas, E. P. & Nixon, L. J. (2005). Towards a Tuplespace-Based Middleware for the Semantic Web, Proceedings of the 2005 IEEE/WIC/ACM international Conference on Web intelligence, (September 19 - 22, 2005). Web Intelligence. IEEE Computer Society, Washington, DC, pp. 338-344 Tolksdorf, R.; Nixon, L.; Bontas, E. P.; Nguyen, D. M. & Liebsch, F. (2005a). Enabling real world Semantic Web applications through a coordination middleware, Proceedings of the 2nd European Semantic Web Conf. ESWC'05, 2005 zur Muehlen, M.; Nickerson, J. V. & Swenson, K. D. (2005). Developing Web Services Choreography Standards - The Case of REST vs. SOAP, Decision Support Systems, Volume 40 , Issue 1, July 2005, Elsevier Science Publishers B. V. Amsterdam, The Netherlands, ISSN:0167-9236

3 Managing Logistics Flows Through Enterprise Input-Output Models V. Albino1, A. Messeni Petruzzelli1 and O. G. Okogbaa2 1DIMeG,

2DIMSE,

Politecnico di Bari, Bari, University of South Florida, 1Italy 2USA

1. Introduction Nowadays, the management of logistics flows is becoming a crucial activity for competitiveness. In fact, globalization is changing the way in which companies organise their production and distribution activities, considerably increasing the spatial complexity of supply chains (see also Choi & Hong, 2002; Stephen, 2004). Therefore, firms have to redesign their supply chains, both global (Meixell & Gargeya, 2005) and local (Carbonara et al., 2001), in order to sustain competitiveness and to deal with the new geography of customers and suppliers (see also Hulsmann et al., 2008; Keane & Feinberg, 2008). In this economic scenario, logistics activities cannot be more considered as a derived demand, but as a key factor for achieving competitive advantage (Hesse & Rodrigue, 2004; Gunasekaran & Cheng, 2008). In fact, the reduction of transportation time and costs can lead supply chains to improve their effectiveness and efficiency. With this regard, in the literature several studies have focusing their attention on the analysis of logistics performance, providing measures and indicators, supporting managers and policy makers in the identification of logistics strategies and policies (see also Lai & Cheng, 2003; Lai et al., 2004). Furthermore, globalization has moved competition from single companies to whole supply chains, thus requiring a joint design and management of logistics flows (Xu & Beamon, 2006; Yi & Ozdamar, 2007). Therefore, in order to guarantee the integrated and effective organization of logistics services, their management and coordination is generally assigned to specific actors, namely third-party logistics (3PL) provider or logistic service provider (LSP) (e.g. Hertz & Alfredsson, 2003; Carbone & Stone, 2005; Kim et al., 2008), which constitute the interconnectedness among the different actors of the supply chain. This new generation of actors is called into being to provide a total logistics service enabling faster movement of goods, shorter turnaround time, more reliable delivery, and reducing the number of transfers. Moreover, the growing attention towards the environmental sustainability has forced organizations to manage their logistics activities evaluating the environmental effects (e.g. Jayaraman & Ross, 2003; Wang & Chandra, 2007). In fact, international trades, global activities of multinationals, and the division of labour/production are strongly increasing

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these negative effects, which are also accentuated by the growing market share of the most energy intensive modes of transportation (truck and air1) and the relative decline of other modes (ship and rail2) (EEA, 2004). The EU White Paper on Transport Policy (CEC, 2001) recognises that transport energy consumption is increasing and that 28% of CO2 emissions are now transport-related. Carbon dioxide emissions continue to rise, as transport demand outstrips improvements in energy-related emissions. The sector with the largest projected increase in EU-15 emissions is transport. In this scenario, consumers and governments are pressing companies to re-design and carefully manage their logistics networks, in order to reduce the environmental impact of their products and processes (Thierry et al., 1995; Quariguasi Frota Neto et al., 2008). In the present paper, we propose the use of enterprise input-output (EIO) models to represent and analyse physical and monetary flows between production processes, including logistics ones. In particular, we consider networks of processes transforming inputs into outputs and located in specific geographical areas. The paper is structured as follows. In the following section, a brief review of EIO models is presented. Then, in Section 3 some possible application fields of EIO models are identified. Sections 4 and 5 describe the basic equations of EIO models and their use. In Section 6 and 7 EIO models are applied to represent and analyse transportation processes, both at an aggregate and disaggregate level, and logistics services markets, respectively. Finally, the main findings and results are summarized into discussion and conclusions (Section 8).

2. Enterprise input-output models The input-output (IO) approach has been typically applied to analyse the structure of economic systems, in terms of flows between sectors and firms (Leontief, 1941). So doing, analysing the interdependencies among entities, economists and managers can evaluate the effect of technological and economic change at regional, national, and international level. According to the different level of analysis, IO models can be highly aggregated or disaggregated. Miller and Blair (1985) use a disaggregated level and consider the pattern of materials and energy flows amongst industry sectors, and between sectors and the final customer. A higher level of disaggregation is useful to define a model better fitting real material and energy flows. However, the drawback of working on a high level of disaggregation is represented by the lack of consistency in the input coefficients. In fact, it is sufficient that technological changes happen in a process to modify the input coefficients. On the other hand, because of the small scale, it is easy to know which technological changes are employed in one or more processes and the modifications to apply to the technical coefficients. EIO models constitute a particular set of IO models, useful to complement the managerial and financial accounting systems currently used extensively by firms (Grubbstrom & Tang, 2000; Marangoni & Fezzi, 2002; Marangoni et al., 2004). In particular, Lin and Polenske (1998) proposed a specific IO model for a steel plant, based on production processes rather than on products or branches. Similarly, Albino et al. (2002, 2003) have developed IO models for analyzing in terms of material, energy, and pollution flows the complex dynamics of 1 2

Air transport is growing by 6–9 % per year in both the old and new EU Member States. The market shares of modes such as rail are increasing only marginally, if at all.

Managing Logistics Flows Through Enterprise Input-Output Models

35

global and local supply chains, and of industrial districts, respectively. Moreover, EIO models based on processes have been adopted to evaluate the effect of different coordination policies of freight flows on the logistics and environmental performance of an industrial district (Albino et al., 2008). At the single firm’s level the EIO model can be useful to coordinate and manage internal and external logistics flows. At the level of the whole industrial cluster the enterprise inputoutput model can be effective to analyse logistics flows and to support coordination policies among firms and their production processes. As in the case of industrial districts, EIO models can be applied to contexts highly characterized by the geographical dimension, such as the local and global supply chains. For better addressing the spatial dimension the EIO approach can be integrated with GIS technology, geographically referring all the inputs and outputs accounted in the models (e.g. Van der Veen & Logtmeijer, 2003; Zhan et al., 2005; Albino et al., 2007). This paper aims at investigating logistics related issues adopting EIO models. To cope with this aim, transportation is modelled as a process (or input) both at an aggregate and disaggregate level, providing the other processes with the logistics services necessary to convey products from origins to destinations. In the former, transportation is modelled as a single process (or input) that supplies all the other production processes involved in the chain. Alternatively, it can be modelled considering all the tracks representing the transportation network through which products flow to and from production processes using the disaggregate approach. These two approaches are used to pursue different system goals. In particular, the aggregate model is used to analyse the logistics flows from a managerial perspective. In fact, economic and operational performance can be evaluated. Whereas, the adoption of a disaggregated approach permits a more space-oriented analysis. Specifically by modelling all the tracks it is possible to examine issues related to traffic congestion, transportation infrastructure availability, and pollutant emissions in specific geographical areas.

3. EIO models for logistics: a framework of analysis As stated in the previous section, EIO models are accounting and planning tools aimed at describing production process and analyzing their reciprocal interdependences. Here, we intend to shed further light on the adoption of EIO models to manage logistics flows, providing a framework that identifies their main application fields and explains their usefulness. In particular, we can consider two main perspectives under which the production processes and related logistics flows can be investigated: i) a spatial and ii) an operational perspective. In the former, the processes are described referring to their location into a specific geographical area. This approach can be effective to examine space-related issues, such as traffic congestion, pollutant emissions, transportation infrastructure, and work force availability. In this case, the analysis is applied to the set ΠG, constituted by all the processes πi (i=1,…,n) located in the area G. Adopting an operational perspective, goals oriented to maximize the efficiency and effectiveness of the processes belonging to a specific supply chain can be pursed. Therefore, the application field is related to the set ΠSC, constituted by all the processes πi (i=1,…,n) belonging to the supply chain SC. Moreover, considering the logistic flows associated to the production processes, a further application can be represented by the analysis of all the

36


flows between processes πi (i=1,…,n) managed by a specific logistic provider. Thus, the set ΠLP, constituted by all the flows ωij (i=1,…,n and j=1,…,m) managed by a specific logistic provider LP, can be studied. These application fields are not mutually exclusive. In fact, they can be combined in order to provide more specific and complex analysis. For instance, we can consider the set ΠG ∩ Πsc, represented by all the processes located in the area G and involved in the supply chain SC. Then, we can describe the generic process πi belonging to this set adopting both an operational and geographical perspective. In particular, all its inputs and outputs are described taking into account the nature and their origins and destinations. In Figure 1, the process πi is represented, identifying its main output (xi), the inputs supplied by other processes belonging to ΠG ∩ Πsc (z1i, z2i,…, zni), the wastes and by products produced by πi (w1, w2,…, wn), and the other primary inputs required by πi and supplied by processes that are not included into the set to ΠG ∩ Πsc (r1, r2,…, rs). w1

w2

wk

wn

z1i

z2i

xi

πi

zki zni

r1

r2

rk

rs

Fig. 1. Inputs and outputs of the process πi. This representation can be useful for accounting purposes, since it permits to identify the outputs produced by the process and all the required inputs. However, in order to take into account the spatial characteristics of inputs and outputs they have to be geographically referred, considering their origins and destinations. All the processes belonging to ΠG ∩ Πsc can be geo-referred as well as the flows between them. In fact, the primary input rk can be supplied by distinct origins. Thus, we can distinguish the input on the basis of its origins, being rkA and rkB, where A and B represent two distinct locations. Moreover, also the main output can be delivered to different destinations. In particular, these destinations can belong or not to the considered set of processes. In the latter, we indicate as fi the output produced by pi and destined outside the boundary of the system. Therefore, the main output can be distinguished on the basis of the destinations. For instance, we can have fiC and fiD. The same consideration can be applied to wastes and by products (wkG, wkF). In Figure 2 the process πi is represented considering the geographical locations of inputs and outputs.

37


wkF

wkE

z1i

zi1 zi2

z2i

πi

zik zin

zki

fiC

zni fiD rkA

rkB

Fig. 2. Inputs and outputs of the process pi, distinguished by geographical locations. Therefore, these two distinct representations permit to move from a physical and monetary description of the processes (Figure 1) to a spatial one (Figure 2).

4. EIO models and production processes: basic equations Let us consider a set of production processes. This set can be fully described if all the interrelated processes as well as input and output flows are identified and modelled. Let Z0 be the matrix of domestic (i.e. to and from production processes within the set) intermediate deliveries, f0 is the vector of final demands (i.e. demands leaving the set), and x0 the vector of gross outputs. If n processes are distinguished, the matrix Z0 is of size n x n, and the vectors f0, and x0 are n x 1. It is assumed that each process has a single main product as its output. Each of these processes may require intermediate inputs from the other processes, but not from itself so that the entries on the main diagonal of the matrix Z0 are zero. Of course, also other inputs are required for the production. These are s primary inputs (i.e. products not produced by one of the n production processes). Next to the output of the main product, the processes also produce m by-products and waste. r0 and w0 are the primary input vector, and the by-product and waste vector of size s x 1 and m x 1, respectively. Define the intermediate coefficient matrix A as follows: A ≡ Z 0 xˆ0−1 where a “hat” is used to denote a diagonal matrix. We now have: x0 = Ax0 + f 0 = ( I − A ) f 0 −1

It is possible to estimate R, the s x n matrix of primary input coefficients with element rkj denoting the use of primary input k (1,…, s) per unit of output of product j, and W, the m x n matrix of its output coefficients with element wkj denoting the output of by-product or waste type k (1,…, m) per unit of output of product j. It results: r0 = R x0

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w0 = W x0 Note that the coefficient matrices A, R, and W are numerically obtained from observed data. A change in the final demand vector induces a change in the gross outputs and subsequently changes in the input of transportation, primary products, and changes in the output of by-products and waste. Suppose that the final demand changes into f , and that the intermediate coefficients matrix A, the primary input coefficients matrix R, and the output coefficients matrix W, are constant (which seems a reasonable assumption in the short-run), then the output changes into: x = ( I − A) −1 f Given this new output vector, the requirements of primary products and the outputs of byproduct and waste are: r = Rx w = Wx where r gives the new s x 1 vector of primary inputs, and w the new m x 1 vector of byproducts and waste types. The enterprise I-O model can be also adopted to account the monetary value associated with each production process. In particular, let p0 be the vector of the prices with element pi denoting the unitary price of the main product at the end of the process i. Thus, considering the vector of the gross outputs x0, we can compute the vector y0, representing the total revenues associated with each gross output as follows: y0 = xˆ0 p0 Moreover, we can define the matrix B, where the generic element bij is expressed as:

bij = aij Then, we have:

pi pj

−1 y0 = By0 + fˆ0 p0 = ( I − B ) fˆ0 p0

If n production processes are considered, the matrix B is of size n x n, and the vectors fˆ0 p0 and y0 are n x 1. Moreover, we can define the vector of the prices p0w , where piw represents the unitary price associated to the wastes and by-products of each process. In particular, waste and by-product will have non-positive and non-negative price respectively. Hence, considering the vector w0 , we can identify the vector y0w representing the total revenues associated with each waste and by-product as follows:

y0w = wˆ 0 p0w Of course, costs are sustained by the production processes. Let in0 be the vector of the costs associated to the primary inputs, including wages and salaries, and an0 the vector of investments amortization. Then, the profit (pt) for all the production processes can be computed as: n

pt = ∑ ( yi + yiw − ∑ p j z ji − ini − ami ) i =1

j

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5. EIO models for a supply chain stage In the present section, we propose a theoretical example, aimed at describing the physical and monetary flows associated with a network of production processes, not including transportation, taking into account the geographical location of inputs and outputs. For the sake of simplicity, a supply chain stage is considered. Let us consider three production processes, π1, π2, and π3, belonging to Πsc and exchanging products as shown in Figure 3. w1

f1

π1

w2

zπ1π3

fπ3 π3

r1

π2

zπ2π3 r2

r1

Fig. 3. Inputs and outputs of production processes in a supply chain stage. Adopting the EIO models, the balance table accounting the materials flows of the supply chain stage is reported in Table 1. Processes π1

π1

π2

π2 π3 Primary inputs r1 r2 Wastes and by-products w1 w2

π3 aπ 1π 3 xπ 3

f0 fπ1

aπ 2π 3 xπ 3

xπ 2 fπ 3

r1π 1 xπ 1

x0 xπ 1 xπ 3

r1π 2 xπ 2 r2π 3 xπ 3

w1π 1 xπ 1 w2π 3 xπ 3

Table 1. Balance table for the supply chain stage in Figure 3. As previously explained, the same type of input and output can be characterised by different origins and destinations. Let us assume that the final demand f3 is delivered to the geographical destinations A and B, the primary input r2 comes from the geographical

40


origins C and D, and the waste w1 is destined to the geographical destinations E and F (Figure 4).

w1F

w1E

f1

π1

w2

zπ1π3

fπ3A π3

r1 zπ2π3

r2D

r2C

π2

fπ3B

r1 Fig. 4. Inputs and outputs of production processes distinguished by geographical origins and destinations. On the basis of this representation, it is possible to define the related balance table, reported in Table 2. Processes π1

π1

π2

π2 π3 Primary inputs r1

π3 aπ 1π 3 xπ 3

f0B

aπ 2π 3 xπ 3

r1π 1 xπ 1

x0 xπ 1 xπ 2

fπ 3 A

fπ 3B

xπ 3

r1π 2 xπ 2

r2C

r2C ,π 3 xπ 3

r2D Wastes and by-products w1E

r2 D ,π 3 xπ 3 w1E ,π 1 xπ 1

w1F

w1F ,π 1 xπ 1

w2

f0A fπ1

w2π 3 xπ 3

Table 2. Balance table for the supply chain stage in Figure 4. Balance tables referring to the monetary flows among processes can be similarly computed.

6. EIO models for logistics flows in a supply chain stage The flows of materials among processes and their final outputs require to be conveyed from origins to destinations. Therefore, in order to effectively describe and analyse the network of

41


production processes, transportation has to be considered. For the sake of simplicity, a supply chain stage is analyzed. In EIO models, transportation can be modelled as: i) a production process or ii) a primary input, which provides other processes with inputs consisting of logistics service, in terms of the distance covered to convey all main products to their destinations. In particular, the transportation system can be modelled as a single production process (T) that supplies all the other production processes involved in the supply chain stage and requires inputs such as workforce, fuel, and energy, as shown in Figure 5.

w1

fπ1

w1

zTπ1

w2

π1 zTπ3 zπ1π3

T

fπ3

π3

r1 zTπ2

zπ2π3 π2 r2

r3 r1

Fig. 5. Inputs and outputs of production processes, including transportation. Following this approach, the balance table can be represented as shown in Table 3. Processes π1

π1

π2

π2

π3 aπ 1π 3 xπ 3

T

aπ 2π 3 xπ 3

r2 r3 Wastes and by-products w1 w2

fπ 3 aTπ 1 xπ 1

aTπ 2 xπ 2

r1π 1 xπ 1

r1π 2 xπ 2

aTπ 3 xπ 3

r3T x T w1T x1 w1π 3 xπ 3

Table 3. Balance table for the supply chain stage in Figure 5.

xπ 3 xT

r1π 3 xπ 3

w1π 1 xπ 1

x0 xπ 1 xπ 2

π3 T Primary inputs r1

f0 fπ1

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Logistics flows can be also modelled adopting a disaggregate approach, i.e. a single transportation process can be associated to each origin and destination materials flow (Figure 6). Moreover, transportation processes can also be distinguished on the basis of the logistics flow, if materials and the trucks load capacity are different. w1

ZT1π1 T1

fπ1

zπ1π3

r3

fπ3 π3

r1 ZT2π2

w1

w2

π1

zπ2π3

ZT3π3

π2

T2

r2 r3

r1

t3

w1

r3

Fig. 6. Inputs and outputs of production processes, including transportation for each origindestination flow. In this case, the balance table is reported in Table 4. Processes π1

π1

π2

π2

π3 aπ 1π 3 xπ 3

T1

T2

T3

fπ 3 aT1π 1 xπ 1

T3 Primary inputs r1 r3 Wastes and byproducts w1 w2

xT2 aT3π 3 xπ 3

r1π 1 xπ 1

xT3

r1π 2 xπ 2 r2π 3 xπ 3

r2

xπ 3 xT1

aT2π 2 xπ 2

T2

x0 xπ 1 xπ 2

a π 2 π 3 xπ 3

π3 T1

f0 fπ1

w1π 1 xπ 1

r3T1 xT1

r3T2 xT2

r3T3 xT3

w1T1 xT1

w1T2 xT2

w1T3 xT3

w 2 π 3 xπ 3

Table 4. Balance table for the supply chain stage in Figure 5.

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As stated at the beginning of the section, transportation can be alternatively modelled as a primary input. Therefore, no inputs, wastes, and by-products related to transportation are considered. In Figure 7 and Table 5, the supply chain stage and the balance table referred to this case are represented. w1

fπ1 rT

w2

π1

rT fπ3

zπ1π3

T

π3

r1 rT

zπ2π3

π2

r2 r1 Fig. 7. Inputs and outputs of production processes, including transportation as a primary input.

π1

π3 aπ 1π 3 xπ 3

π2

a π 2 π 3 xπ 3

Processes

π3 Primary inputs r1

π1

π2

r1π 1 xπ 1

r1π 2 xπ 2

rTπ 1 xπ 1

rTπ 2 xπ 2

x0 xπ 1 xπ 2

fπ 3

xπ 3

r2π 3 xπ 3

r2 T Wastes and by-products w1

f0 fπ1

rTπ 3 xπ 3

w1π 1 xπ 1

w2

w 2 π 3 xπ 3

Table 5. Balance table for the supply chain stage in Figure 7. Also in this case, logistics flows can be modelled using a disaggregate approach, distinguishing different transportation inputs, according to the origin-destination materials flow. The proposed EIO models can be adopted to analyse the logistics flows of a supply chain stage located in a specific geographical area. Therefore, we can consider a set of production processes belonging to ΠG ∩ Πsc.

44


However, these models are not able to make distinction about primary inputs, wastes, byproducts, and outputs transportation. To make distinction, we add virtual processes located within the considered geographical area G or on its boundaries, depending on where the primary input is available (within or outside the area). Each virtual process, corresponding to a specific primary input, is characterised by geographical information about its location and it has an output that can be transported to all the production processes requiring that input. For each virtual process no inputs are allowed from the production processes. Let us consider h virtual processes corresponding to s primary inputs from outside the geographical system. Then, we introduce Z 0* and x0* as the matrix of domestic intermediate deliveries and the vector of gross outputs including the h virtual processes, respectively. If n processes are distinguished, including transportation processes, the matrix Z 0* is of size (n+h) x (n+h) and the vector x0* is (n+h) x 1. Define the intermediate coefficient matrix A* as follows:

A* ≡ Z 0* xˆ0−1* The apex * can be extended with similar meaning to all variables as needed. The same approach can be used to model wastes and by-products transportation. Let us consider two production processes, πj and πk, two virtual processes, v1 and v2, corresponding to two primary inputs, r1 and r2, respectively, and the process T having, for the sake of simplicity, no intermediate deliveries from processes πj and πk, and no primary inputs. Moreover, each process, primary input, waste, and by-product is characterised by a single location, and no imports are considered from outside G, unless the two primary inputs. Finally, let us assume that the final demand fπk is delivered to the geographical destination A and the waste w1 is destined to the geographical destination B (Figure 8). w1B w1B

πj zTπj

zTv1

v1

πk

zTπk

T

zv1πj

fπkA

zπjπk

w1B zv2πk

zTv2

w1B

w1B

v2

r2 r1 Fig. 8. Inputs and outputs of production processes, including transportation and virtual processes.

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In Table 6 the balance table referred to the supply chain stage depicted in Figure 8 is reported.

πj

Process πj

πk aπ jπ k xπ k

T

v1

v2

πk T

aTπ j xπ j

v1

a v1π j xπ j

aTπ k xπ k

x0 xπ j

fπ k A

xπ k xT x v1

a v2π k xπ k

v2 Primary inputs r1 r2 Wastes and byproducts w1B

f0A

xv2 r1v1 x v1 r2 v2 x v2

w1B ,π j xπ j

w1B ,π k xπ k

w1B ,T xT

w1B , v1 x v1

w1B ,v2 x v 2

Table 6. Balance table for the supply chain stage in Figure 8. As previously explained, the main output of process T is represented by the total distance covered by transportation means to deliver products from origins to destinations. Thus, considering the distance between the processes, as provided in Table 7, we can compute, for zπ π instance, zTπ j as: zTπ = d1 ⋅ C where C represents the transportation means’ load capacity. j k

j

πj

From/to πj πk v1 v2

πk d1

d1 d2 d2

d4 d4

v1 d2 d4

v2 d3 d5 d6

d6

Table 7. Distance between processes. Moreover, the distances between the processes can be distinguished into the different paths covered to convey products, which are constituted by the track connecting the processes, as shown in Table 8. From/to πj πk v1 v2

πj

πk θ1-θ2

θ2-θ1 θ3-θ1 θ4-θ1

θ3-θ2 θ4-θ2

v1 θ1-θ3 θ2-θ3

v2 θ1-θ4 θ2-θ4 θ3-θ4

θ4-θ3

Table 8. Paths covered by transportation means. Therefore, zTπ results: j

zTπ = ( dθ + dθ ) ⋅ j

1

2

zπ π

j k

C

= dθ ⋅ 1

zπ π

j k

C

+ dθ ⋅ 2

zπ π

j k

C

46


where dθ and dθ represent the lengths of the track θ1and θ2, respectively. 1

2

On the basis of these considerations, the transportation process can be modelled adopting a disaggregate approach into l processes ( θ k , k=1,…, l), corresponding to the l tracks covered by transportation means to deliver products. In this case, the balance table reported in Table 4 can be described as in Table 8. Process

πj

πk

θ1

θ2

θ3

θ4

v1

v2

πj

xπ j fπ k A

πk θ1

aθ1π j xπ j

θ2

aθ2π j xπ j aθ2π k xπ k

aθ1v1 xv1 aθ3v1 xv1

av1π j xπ j

v2

xθ4 x v1

av2π k xπ k

xv2

Primary inputs r1 r2 Wastes and by-products w1B ,π j xπ j w1B

xθ2 xθ3

aθ4v2 xv2

θ4

xπ k

xθ1 aθ2v2 xv2

θ3

v1

f0A x0

aπ jπ k xπ k

r1v1 x v1 r2 v2 x v2

w1B ,π k xπ k w1B ,θ1 xθ1 w1B ,θ 2 xθ 2 w1B ,θ 3 xθ 3 w1B ,θ 4 xθ 4 w1B , v1 x v1 w1B ,v2 x v 2

Table 8. Balance table in the disaggregated representation of transportation processes. Adopting the disaggregate approach, each transportation input is related to a given track θ k (k=1,…, h). Then, each process delivering products to two or more final destinations can be distinguished according to their final destinations, maintaining the same geographical location. In fact, let us assume to have three production processes (π1, π2, and π3) and two tracks ( θ1 and θ 2 ), with the balance table reported in Table 9, where θ1 and θ 2 connect π1 with π2, and π1 with π3, respectively.

Table 9. Balance table.

π1 π2 π3 θ1

π1 0 0 0 t11

π2 z12

π3 z13

0 0 0

0 0 0

θ2

t21

0

0

F 0 f2 f3

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If f2 increases, then z12 must increase and, consequently, x1 increases. However, only t11 must increase to permit to serve more output of π1 to π2. Then, the process p1 has to be distinguished into p12 and p13, according to its final destinations, as shown in Table 10.

π12

π12 0

π13 0

π2 z12

π3 0 z13

π13 π2 π3

0 0 0

0 0 0

0 0 0

θ1

t1,12

0

0

0

θ2

0

t2,13

0

0

0 0

F 0 0 f2 f3

Table 10. Balance table in the case of process π1 distinguished according to its final destinations. Now, t1,12 and t2,13 represent the distance covered by the transportation mean to convey the output of process π1 to the process π2 through the track

θ1, and the output of process π1 to

the process π3 through the track θ2, respectively. Finally, it is important to compute and geo-refer the pollution caused by the transportation means along the tracks. Then, let us define the waste vector w0θ of size m x 1 and the m x h matrix W θ of output coefficients with element wkjθ denoting the output of waste type k (1,…,m) per unit of transportation input j. It results:

w0θ = W θθ 0 Then, pollution caused by transportation can be easily computed and geo-referred.

7. EIO models and logistics services markets As stated in Section 1, logistics services are generally managed by logistics providers (3PL), which own the key competencies and capabilities necessary to assure their effectiveness and efficiency. Referring to transportation services, three distinct actors can be identified: i) suppliers, which have to deliver products to one or more customers; ii) customers, which require products from one or more suppliers; iii) 3PL providers, which provide the transportation service and coordinates logistics flows between suppliers and customers. The interaction among these different actors represents what is generally defined as a logistics services market. In particular, it can be composed by actors belonging both to the same companies, or different and independent ones. Adopting EIO models it is possible to describe these markets modelling each actor as a different production process. Let us consider three suppliers (S1, S2, and S3), two customers (C1, C2, and C3), and two logistics providers, which are represented by two distinct transportation processes (T1, and

48


T2), forming the logistics services market depicted in Figure 9. For the sake of simplicity, no primary inputs, wastes, and by-products are considered. zT1C1 zT1S1

zS1C1 S1

T1

fC1

C1 zS2C1 S2

zT1S2

fC2 C2

T2

zS3C2 S3 zT2S3

zT2C2

Fig. 9. A logistics services market. The EIO model permits to account the demand and supply of logistics services, as shown in the balance Table 11.

S1

C1 zS C

S2

zS C

Process

S1

S2

S3

C2

T1

T2

f0

1 1

1

xS

2 1

zS C

S3

x0 xS

2

xS

3 2

3

C1

fC

1

xC

C2

fC

2

xC

T1 T2

zT S

1 1

zT S

zT C

1 2

1 3

2

xT

1 1

zT S

1

1

zT C

1 2

xT

2

Table 11. Balance table for the logistics services market in Figure 9. This representation can be useful also as a planning tool, to analyse the economic and environmental performance of logistics services markets, investigating how it is affected by the different degree of cooperation among the actors. In fact, different market organizations


49

can be proposed and investigated, on the basis of the collaboration degree of the actors, and following approaches aimed at minimizing the number of trips, such in as in the case of consolidation strategies, and at creating logistics networks specialized, for instance, by geographical areas, types of product, and services.

8. Discussion and conclusions The present paper has proposed the use of EIO models to describe and analyse logistics flows to support managers and policy makers in the definition of policies for their management and coordination. In particular, different approaches and models have been proposed. Two main perspectives have been used to analyse logistics flows, such as a spatial and operational one, pursuing different goals. In fact, geo-referring the production processes belonging to a supply chain, and their inputs and outputs, spatial-oriented analyses can be performed in order to deal with issues related to traffic congestion, pollutant emissions, transportation infrastructures, and work force availability. Therefore, on the basis of these analyses policies aimed at improving the transportation sustainability and reducing its negative impact on the environment can be identified. Differently, the adoption of an operational framework of analysis can permit to describe logistics flows involved in specific supply chains, or, more in detail, managed by specific actors, in order to analyse and improve logistics economic performance. For instance, solutions aimed at consolidating the flows and reducing the number of trips and the transportation costs can be achieved. Moreover, transportation has been modelled both as a process and as a primary input. The difference between these two approaches depends on the inclusion of inputs required and wastes produced by transportation. In fact, in the former all the transportation inputs, such as workforce and fuel, and its pollutant emissions are considered. Therefore, this approach can be useful for 3PL to evaluate the economic and environmental performance of their activities. On the contrary, the modelling of transportation as a primary input can be used to investigate its impact on the supply chains. In fact, in this case the model can be a suitable accounting and planning tool for actors representing the demand of logistics services, in order to analyse, for instance, how logistics affects their profit and operations. Transportation has been also modelled at an aggregate and disaggregate level. The aggregate level of analysis permits to investigate managerial-oriented issues, giving a holistic view of the transportation to evaluate its global role on the supply chains’ efficiency and effectiveness. Differently, the disaggregate approach permits a more in-depth analysis, since it describes all the tracks covered by transportation means, thus allowing policy makers to propose actions for the logistics management according to a social and environmental perspective. Finally, EIO models have been used to represent logistics service markets, in order to account and analyse the demand and supply of transportation. This can be useful for both logistics providers and customers to organize markets, on the basis of different criteria (e.g. consolidation, specialization, geographical areas) and to improve their performance. The present study contributes to extend the existing framework on logistics management, providing a set of complete and complementary tools, based on the use of EIO models, able

50


to analyse the problem according to different perspectives and point of views. The power of the described methodology is strictly related to the possibility to investigate the impact of logistics on the whole performance systems, thanks to the direct and indirect relationships and interdependences among production processes. Further researches should be devoted to apply the models to actual cases, in order to show their effectiveness as both accounting and planning tools and to strength the relevance of the proposed approach.

9. References Albino, V., De Nicolò, M., Garavelli, A.C., Messeni Petruzzelli, A., Yazan, D.M. (2007) Rural development and agro-energy supply chain. An application of enterprise inputoutput modelling supported by GIS, 16th International Input-Output Conference, July 2-6, Istanbul, Turkey. Albino, V., Dietzenbacher, E., Kühtz, S. (2003) Analyzing Material and Energy Flows in an Industrial District using an Enterprise Input-Output Model, Economic Systems Research, Vol. 15, pp. 457-480. Albino, V., Izzo, C., Kühtz, S. (2002) Input-Output Models for the Analysis of a Local/Global Supply Chain, International Journal of Production Economics, Vol. 78, pp. 119-131. Albino, V., Kuhtz, S., Messeni Petruzzelli, A., (2008) Analysing logistics flows in industrial clusters using an enterprise input-output model, Interdisciplinary Information Sciences, Vol. 14, pp. 1-17. Carbonara, N., Giannoccaro, I., Pontrandolfo, P. (2001) Supply chains within industrial districts: theoretical framework, International Journal of Production Economics, Vol. 76, pp. 159-176. Choi, T.Y Hong, Y. (2002) Unveiling the structure of supply networks: case studies in Honda, Acura, DaimlerChrysler, Journal of Operations Management, Vol. 20, pp. 469–493. Commission of the European Communities (2001) European Transport Policy for 2020: Time to Decide (Luxembourg: Office for Official Publications of the European Communities). European Energy Agency (2004) EEA signals 2004, A European Environment Agency Update on Selected Issues (Copenhagen: European Energy Agency). Grubbström, R.W., Tang, O. (2000) An Overview of Input-Output Analysis Applied to Production-Inventory Systems, Economic Systems Research, Vol. 12, pp. 325. Gunasekaran, A., Cheng, T.C.E. (2008) Special issue on logistics: new perspectives and challenges, Omega, Vol., 36, pp. 505-508. Hertz S., Alfredsson M. (2003) Strategic development of third party logistics provider, Industrial Marketing Management, Vol. 30, pp. 139-149. Jayaraman, V., Ross, A. (2003) A simulated annealing to distribution network design and management, European Journal of Operational Research, Vol. 144, pp. 629-645.


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Kim, C., Yang, K.H., Kim, J. (2008) A strategy for third-party logistics systems: a case analysis using the blue ocean strategy, Omega, Vol. 36, pp. 522-534. Lai, K.H., Cheng, T.C.E. (2003) A Study of the Freight Forwarding Industry in Hong Kong, International Journal of Logistics: Research and Applications, Vol. 7, pp. 7284. Lai, K.H., Ngai, E.W.T., Cheng, T.C.E. (2004) An empirical study of supply chain performance in transport logistics, International Journal of Production Economics, Vol. 87, pp. 321-331. Leonief, W.W. (1941) The Structure of the American Economy. Oxford University Press, New York. Lin, X., Polenske, K.R. (1998) Input-Output Modelling of Production Processes for Business Management, Structural Change and Economic Dynamics, Vol. 9, pp. 205-226. Marangoni, G., Colombo, G., Fezzi, G. (2004) Modelling Intra-Group Relationships, Economic Systems Research, Vol. 16, pp. 85-106. Marangoni, G., Fezzi, G. (2002) I-O for Management Control: The Case of GlaxoSmithKline, Economic Systems Research, Vol. 14, pp. 245-256. Meixell, M.J., Gargeya, V.B. (2005) Global supply chain design: a literature review and critique, Transportation Research, Vol. 41, pp. 531-550. Miller, R.E., Blair, P.D. (1985) Input Output Analysis: Foundations and Extensions. Prentice Hall, Englewood Cliffs. Quariguasi Frota Neto, J., Bloemhof-Ruwaard, J.M., van Nunen, J.A.E.E., van Heck, E. (2008) Designing and evaluating sustainable logistics networks, International Journal of Production Economics, Vol. 111, pp. 195-208. Stephen, T. (2004) Further reflections on the supply chain complexity gap, Supply Chain Europe, Vol. 13, pp. p. 38. Thierry, M., Salomon, M., van Nunen, J.A.E.E., van Wassenhove, L. (1995) Strategic issues in product recovery management, California Management Review, Vol. 37, pp. 114135. Van der Veen, A., Logtmeijer, C. (2003) How vulnerable are we for flooding? A GIS approach, (No. EUR Report 20997 EN). Office for Official Publications of the European Communities, European Commission, Brussels. Wang, Y., Chandra, L.S. (2007) Using e-business to enable customised logistics sustainability, International Journal of Logistics Management, Vol. 18, pp. 402419. Xu, L, Beamon, B.M. (2006) Supply chain coordination and cooperation mechanisms: an attribute-based approach, The Journal of Supply Chain Management, Winter 2006, pp. 4-12. Yi, W., Ozdamar, L. (2007) A dynamics logistics coordination model for evacuation and support in disaster response activities, European Journal of Operational Research, Vol. 179, pp. 1177-1193.

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Zhan, G., Polenske, K.R., Ning, A. (2005) Evaluation of yellow-dust storms and countermeasure policies in North China using regional input-output models, 15th International Input-Output Conference, 27 June – 1 July, Beijing, P.R. of China.

4 Dynamic Analysis and Control of Supply Chain Systems Alejandro Rodríguez-Angeles, América Morales Díaz and Arturo Sánchez

Centro de Investigación y de Estudios Avanzados Depto. Ing. Electrica, Unidad Saltillo & Unidad Guadalajara México

1. Introduction Globalization has changed in the last decade the shape of world trade, from independent and local markets to highly interrelated value chains demanding a large variety of goods with high quality standards. A supply chain is thus understood as a highly interconnected demand network composed of different stages or nodes that include raw materials, distributors of raw materials, manufacturers or producers, distributors of manufactured products, retailers and customers. Between these stages or nodes there is an interchange of information (orders) and flows of materials. The above mentioned elements of a supply chain and their relations give rise to complex structures, whose interactions affect the performance of the entire system. Nowadays the markets require flexibility, speed and productivity in order to satisfy an environmentally-conscious consumer demanding a larger variety of manufactured goods. These trade conditions impose low costs and effectiveness in production. Therefore, most supply chains strive for minimizing raw material and finished products inventories in fast distribution networks (Fujimoto, 2002), (Steidtmann, 2004) while fulfilling astringent manufacturing rules. This tendency has resulted on what is called synchronization of production and distribution at the supply chain. Supply chain synchronization occurs when the consumer business world is linked together by technology, making each of the constitutive parts, consumers, suppliers, producers, associates and distributors synchronize with the whole. Thus, when the consumer places a request for an end-product, there is a synchronized retailer or distributor there to deliver it. For example (Koudal, 2003) studied the demand and supply dynamics in the automotive value chain, yielding flexibility and fast consumer respond. In textile industry, TAL Appareal Group applied electronic and communications platforms to evolve into a flexible manufacturer, growing from a single local textile mill to a global multinational company (Koudal & Wei-teh, 2005). From an economic point of view the potential impact of performance improvements on production systems is tremendous. The arising dynamics complexity of highly synchronized supply chains poses demanding control challenges for their operation and management, for which representative models are required. Two decision levels can be considered in the supply chain operation: the first is the tactical and is referred to the decision making process that optimizes the supply chain performance, and the second involves the operational activities. A well-constructed supply

54


chain model, either for the tactical or operative levels, must preserve relevant information associated with the flow and transformation of raw materials to finished products and their distribution to the consumers. Several approaches in supply chain modelling have been considered, from quantitative models (Tayur et al., 1998) to fuzzy models (Petrovic et al., 1999). Extensive overviews can be found in (Daganzo, 2002) and (Shapiro, 2007). Most of the proposed supply chain models are deterministic, nonetheless there are a few that takes into account demand uncertainty in an stochastic framework (e.g. Lababidi et al., 2004) . Supply chain management has been extensively studied. Most works focused on the tactical area and mainly deal with production planning and scheduling. In planning and scheduling of supply chains one of the goals is to determine the inventory levels to satisfy the market demands in a timely and cost effective manner. Inventory levels planning and orders handling are addressed in (Perea et al., 2001) and (Cetinkaya & Lee, 2000). In (Perea et al., 2001) a dynamic model based on balances of inventories and orders is presented, together with a decentralized decision making process that considers production policies. The same model is used in (Perea et al., 2003) to develop a model predictive control strategy, improving the planning forecast in a time horizon. Some others works are focused on the supply chain dynamics and its operational level. In (Lin et al. 2004) an analysis and control for bullwhip effect is presented based on ztransform. By using traffic dynamics (Nagatani & Helbing, 2003) proposed a continuous time dynamic model to represent the behaviour of the inventories and the production rates. Moreover a control strategy and production policies to bring an unstable system into the stable region are proposed. The aim is to control the production rates, while inventories are, to some extend, free to evolve in a bounded region, which may result in unsuitable stock levels regarding a reliable and competitive performance of the system. Analytical conditions for absolute and convective instabilities were presented in (Helbing et al., 2004). Finally, for the dynamic management of supply chain networks, (Dunbar & Desa, 2005) demonstrated the application of nonlinear distributed model predictive control strategies. (Lefeber, 2004) also considers traffic flow theory to propose a supply chain model based on differential equations, taking into account delays on handling orders and inventory level dynamics. This chapter is focused on the operational activities of the supply chain dynamics. Similarly to those works developed in (Perea et al., 2001), (Helbing et al., 2004) and (Lefeber, 2004), dynamic models for multipurpose systems are proposed considering production ratios. First a dynamic model dealing only with material flows is introduced, and then it is extended to consider order flows and delays on handling materials and orders. In agreement with physical limitations, bounds in the inventory levels and flow rates are taken into account. Once the models are introduced, a bounded and smooth controller is proposed. This controller regulates the inventory levels while synchronizing the flows at the whole supply chain system. Closed loop convergence to desired inventory levels and production rates stability is proved. The proposed modelling and control techniques are applied to two simulation case studies: a petrochemical multiproduct plant and a crude oil blending and distribution system. The chapter closes with some general conclusions and trends in supply chain systems.

2. Supply chain dynamic models A supply chain model must capture relevant activities associated with the inventory levels, and the flow and transformation of good from the raw material stage to the final costumer.

Dynamic Analysis and Control of Supply Chain Systems

55

The entities that may form a supply chain, i.e. suppliers, distributors, producers, etc., they can be classified as producer or non producer nodes. The main difference among them is that producer nodes may vary their production ratio, thus varying its inventory level. Non producer nodes may vary their inventory levels only by ordering from upstream entities. Although inventory and material flow dynamics may be enough for modelling some supply chain systems, there are situations in which information flows, such as orders, are needed to properly represent the system dynamics. This section presents both types of models, one entirely based on inventory levels and material flows, and one that considers orders handling. 2.1 Linear supply chain systems: material flow rate model The models are based on macroscopic mass balance equations. Therefore the mass variation is equal to the difference between the material inflow and outflow rate (see Figure 1) and is represented by equation (1). These models represent systems in which the material delivery flow rate corresponds exactly and without delay to the demanded one. Notice that the above considerations render linear dynamic models based on differential equations.

Fig. 1. Mass balance representation in a supply chain node. By considering the dynamics on the properties of interests, i.e. inventory levels and material flows, a supply chain consists from n-nodes, with inventory levels N i , for i = 1,… , n , and depending if the node is a producer or not, a production or incoming flow rate λi; as it is depicted in Figure 2. The change in the inventory level is represented by the difference between the production or incoming rate λi, depending whether it is a producer or non producer node respectively, and the delivery flow rate λd _ p ,i .

dN i = λi − λd _ p ,i dt

(1)

Where λd _ p ,i represents the total demand of products to node i , and considers the demand of all the r nodes requiring products or material from node i , with individual demanding rates λi and with the product ratio Fi , j , this is r

λd _ p ,i = ∑ Fi , jλ j

(2)

j =1

The product ratio Fi,j allows modelling multipurpose or multiproduct systems. The product ratios represent the amount of product i that is required by the j-node to produce a unit of product. The product ratio allows changing recipes or product proportion, such that

56


different products can be easily considered. Furthermore, these product ratios define the synchronized behaviour among the material flows at the supply chain.

Fig. 2. Inventories, information and material flow in a supply chain. The production or incoming rate λi constitutes the control action to vary the inventory dynamics in a node- i , whether it is a producer or non producer node respectively. For a non producer node its incoming rate corresponds only to the product or materials that are received from its suppliers. Contrary, in a producer node the production rate varies accordingly to production policies and posses dynamical behaviour. In a producer node a change in the production rate involves several activities that require an adaptation time Ti. Moreover, if Wi denotes the control action that varies the production rate λi in a producer node, then its dynamics can be represented by

dλi 1 = (Wi − λi ) dt Ti

(3)

Notice that because of physical or operational boundedness of the system the inventories, production and incoming rates, must be bounded accordingly, this is

N i ,min ≤ N i ≤ N i ,max 0 ≤ λi ≤ λi ,max

(4)

where N i ,min , N i ,max are the minimum and maximum allowed inventory level respectively, and λi ,max is the maximum production or incoming rate. In case of producer nodes, their dynamics includes inventory levels and production rate, thus, they are modelled by equations (1), (2) and (3). Meanwhile for non producers only inventory levels dynamics (1) and (2) are considered, which vary thru ordering products or material from upstream nodes. The model represented by equations (1)-(4) disregard delivering delays, nevertheless, the adaptation time Ti plays a role that induces a kind of delay. The adaptation time parameter Ti allows reproducing bullwhip phenomena among other phenomena in this type of model.


57

At model (1), (2), (3) and (4) raw materials storage at a producer node is consider as an independent entity, which renders extra degrees of freedom to the system regarding control policies. This consideration establishes an advantage and at the same time a challenge that allows regulating inventory levels and simultaneously supply chain synchronization. The consumer demand is the variable that imposes the policy operation and is traduced to flow rates for each node, taking into account the production ratios Fi,j. For example, in the automotive supply chain several types of cars have to be built, the net flow of finished cars per time unit is the costumer demand. But at upstream entities of the supply chain this is traduced in the corresponding part rate, e.g. 4 wheels and one motor per car, and so on. 2.2 Nonlinear supply chain systems: orders flow model A supply chain as that depicted in Figure 2 includes information flows, particularly orders among downstream and upstream entities. In some situations such information flows have to be taken into account, either to strive for performance improvement or because not all orders are fulfil immediately, then an unattended orders balance appears. Accumulation of material Ni and unattended orders Oi can be modelled similarly as balances between incoming and outgoing flows as follows

dN i = λi − y i dt

(5)

dOi = δ i − yi dt

(6)

Where yi represents the shipped product (attended orders) and i represents the total product demanding rate, which correspond to all orders requiring product or material from the ith-node. Similarly to the total product demand (2) product ratios Fi,j may be considered, this is r

δ i = ∑ Fi , jλ j j =1

In the above model representation (5), (6), when an order is put in node i , it will take an interval of time until it is attended, this is represented by a time delay parameter denoted by i. Furthermore, following traffic flow theory (Lefeber, 2005) a shipping function that depends on the inventory level and the accumulated orders is introduced, this is

yi =

N i Oi ( N i + 1) τ i

(7)

Equation (7) reflects the outgoing product rate in node i and it is bounded from below by yi ≥ 0 , thus only positive flows are allowed. The outgoing product policy depends on inventory level Ni such that the product will be shipped only if there is in stock. Since (7) is a nonlinear function on Ni, then the resulting supply chain model is of nonlinear type. The production rate λi in a producer node, as in the case of model (1), (2), (3) and (4), varies accordingly to production policies. Therefore the dynamics in the flow rate is the same as in

58


equation (3). For simplicity in case of producer nodes it is assumed that upstream nodes deliver the demanded product or material as soon as it is required for production; this is a producer has storage facilities which guarantee the required materials. Then these storage facilities are the ones that can have unattended orders from their suppliers. Furthermore, it is considered that producer nodes modify its production rate to deliver the material amount that is required such that orders accumulation is not an issue in a production facility. Then a producer node is modeled by equation (5), (3), and (7) with yi = i. For non producer nodes, their dynamics is given only by inventory levels and balance of unattended order, equations (6) and (7). Therefore accumulation of unattended orders appears only in non producer nodes.

3. Supply chain regulation, control strategies When regulating the supply chain dynamics, the goal is to establish flow rates that guarantee that the inventory levels reach and keep a desired level, while a final costumer demand is satisfied. On the other hand, as it was mentioned the supply chain must satisfy physical constraints, such as bounded inventory levels, and maximum production or incoming flow rates, see equation (4). Therefore the control actions to regulate the supply chain must be designed taking into account such constraints. This section presents two bounded control strategies for inventory regulation and flow rate synchronization. For these purposes PI techniques are considered to vary the production or incoming flow rates. Stability is proved by standard analysis based on linearization theory and pole placement techniques. First the controller for linear supply chain systems is presented and then it is extended for nonlinear supply chains. 3.1 Control strategy by flows variation For producer nodes, variation on the production rate λi, whose dynamics is given by equation (3), is achieved by the control action or production policy Wi. Meanwhile for non producer nodes, inventory levels regulation is achieved by direct variation of their incoming flow rates λi. For each of these two classes of systems similar control actions are proposed as follows. Since the production rate λi in (2, 3) is bounded by (4), then the control action must be bounded accordingly at a producer node, this is

0 ≤ Wi ≤ λi ,max

(8)

To satisfy the above constraint the control action Wi for a producer node is proposed as

1 1 ⎞ Wi = λi ,max ⎛⎜ 2 − −α N ⎟ −α i ( N i − N c , i ) − 1 + e i c ,i ⎠ 1+ e ⎝

(9)

Meanwhile for a non producer node its inventory dynamics (1) is modified by its incoming rate λi, such that it is proposed to vary as

λi = λi ,max ⎛⎜ 2 − ⎝

1 1+ e

−αi ( N i − Nc , i )

−

1 ⎞ −α N ⎟ 1 + e i c ,i ⎠

(10)

59


where λi,max is the maximum production or incoming rate in the node, see (4), α i is a parameter that regulates the convergence rate of Ni. The larger α i, the faster the convergence rate of Ni to its desired value Nd,i. However, too large values of α i can induce instability. Nc,i is proposed as a modified PI control, it acts as a nominal reference and forces Ni to a desired constant value Nd,i. Nc,i allows introduction of several production policies as the ones presented in (Helbing, 2003).

N c ,i = N d ,i − K P ,i ( N i − N d ,i ) − K I ,i ∫ ( N i − N d ,i )dt

(11)

Where KP,i and KI,i are the positive proportional and integral control gains, respectively. The integral action renders a steady error equal to zero around the equilibrium point, while the proportional action regulates the convergence rate. The controllers (9) and (10) use exponential functions to render a bounded control action, while allowing a smooth and fast convergence. The term in between parenthesis is bounded in [0,1], after multiplying this term by λi,max, the physical and operational limitations on the production or incoming rate, see (4), are recovered. Stability analysis The goal of this section is to establish closed loop stability and synchronization conditions for the supply chain at an equilibrium point. The controllers (9) and (10) induce nonlinearities in the closed loop system, so that, linearization techniques are considered for the stability analysis. Theorem 1 The equilibrium point ( λi∗ , N i∗ ) of the closed loop formed by a producer node (1 - 3) and (9) is given by λi∗ = λd _ p ,i for the production rate, and N i∗ = Nd ,i for the inventory level. Meanwhile, the closed loop of a non producer node (1) and (10) implies the same equilibrium point as for a producer node, it is λi∗ = λd _ p ,i and N i∗ = N d ,i . Proof: First for a producer node, from (1) and (3) it follows that the equilibrium conditions are

0 = λi∗ − λd − p ,i

0=

1 ( Wi − λi∗ ) Ti

(12)

thus λi∗ = λd _ p ,i and simultaneously Wi = Wi ( N i∗ ) = λi∗ such that Wi ( N i∗ ) must be constant. By substitution of (9) and (11), it follows that because λi ,max and α i are constants, then Wi ( N i∗ ) is constant if and only if N i∗ = N d ,i . A similar analysis allows concluding the closed loop equilibrium point for non producer nodes. ■ Note that the equilibrium point of both producer and non producer nodes implies that λi∗ = λd _ p ,i , therefore λi synchronizes with the total demanding rate λd _ p ,i , given by equation (2), and such that instantaneous consumption and supply chain synchronization are achieved. Simultaneously the inventory levels fulfills N i∗ = N d ,i , thus inventory regulation is obtained. Theorem 2 The closed loop system formed by a producer node (1 - 3) with the controller (9) and (11) is locally asymptotically stable and converge to the equilibrium point λi∗ = λd _ p ,i and N i∗ = N d ,i , if the gains α i , KP ,i , and KI ,i satisfy

60


αi ≥ 1

(13)

ζ 1 ≤ K P ,i < ζ 2

(14)

1 K P ,i 4 Ti

0 < K I ,i ≤

ζ1 =

(

(15)

) (α λ T − 1) ) α λ T (e ( − 1) ) 1+ e ( ) ( ζ = (e ( ) − 1)

−αN 1 + e ( i d ,i )

2

i i ,max i

2

− αi N d , i

i i ,max i

− αi Nd , i

2

− αi Nd , i

2

2

Furthermore, the above conditions ensure an overdamped closed loop system, (i.e. the eigenvalues are negative and purely real), avoiding large overshoots and keeping physical and operations constraints given by (4). Proof: In the equilibrium point λi∗ = λd _ p ,i , N i∗ = N d ,i , the closed loop is linearize around small deviations δλi and δ N i as

where x = [δ N i

0 1 ⎤ ⎡ ⎢ ⎥x x= 1 ⎢ Wi '( N i ∗ ) − 1 ⎥ Ti ⎦⎥ ⎣⎢ Ti

(16)

T δλi ] and whose eigenvalues s1,2 are given by

− s1,2 =

1 1 4 ± + Wi '( N i∗ ) Ti Ti 2 Ti 2

(17)

Then the closed loop is asymptotically stable and overdamped if the derivative of the control function Wi '( N i ∗ ) fulfills

−

1 ≤ Wi '( N i∗ ) < 0 4Ti

(18)

which imposes conditions on α i , KP ,i , and KI ,i . Replacing (11) in (9) yields

⎛ ⎞ −αN KP ,i + tK I ,i ) e ( i d ,i ) ⎟ ( ⎜ 1 Wi '( N i ) = α iλi ,max ⎜ − ( 1 + KP ,i + tK I ,i ) + 2 ⎟ −αN 1 + e ( i d ,i ) ⎜ 4 ⎟ ⎝ ⎠ ∗

(

)

(19)

61


where t represents the integration time. When N i∗ = N d ,i there is not integral action, thus it can be taken t = 0 , and from (19) and by algebraic manipulation it follows that sufficient conditions on KP ,i , for (18) being satisfied, are given by (14). By defining the regulation error ei = N i − N d ,i , replacing it in (11) and taking first derivative with respect to time it is obtained

dN c ,i de = −α i KP ,i i − K I ,i ei dt dt

(20)

dN c ,i de = −α i KP ,i i − K I ,i ei dt dt

(21)

In the equilibrium point N i becomes constant, i.e. N i∗ → N d ,i , thus (20) equals to zero and by Laplace transform it is obtained the pole

Considering that the pole in (21) must verify condition (18) to limit the dynamics of the closed loop, then KI ,i must satisfy condition (15). Finally, since only KP ,i through the condition (14) depends on α i , it can be to some extend freely chosen. Thus for convenience and to obtain fast convergence it is taken that αi > 1 . ■ Theorem 3 The closed loop system formed by a non producer node (1) with the controller (10) is locally asymptotically stable and converge to the equilibrium point λi∗ = λd _ p ,i and N i∗ = N d ,i , if the gains α i , KP ,i , and KI ,i satisfy

αi ≥ 1 ) 1+ e ( ) ( < (e ( ) − 1) − αi Nd ,i

KP ,i

− αi Nd ,i

(22) 2

2

K I ,i > 0 Furthermore, the above conditions ensure an overdamped closed loop system. Proof: It follows as for Theorem 2

(23)

(24)

■

3.2 Control strategy by orders and flows handling Due to the similarities between the models for linear supply chain systems (1 – 4) and the model that considers orders handling (5, 6, 3 and 7), the controllers (9, 10 and 11) still apply. This section shows that the unattended orders modify the closed loop equilibrium point and the value conditions on the control gains, but the control architecture (9, 10 and 11) is robust enough to deal with order handling and the implicit delays modelled by the shipping function yi given by (7) and the production adaptation parameter Ti. Stability analysis The stability analysis follows as the one presented in Section 3.1, first the equilibrium point is determined and then stability and convergence conditions are provided.

62


Theorem 4 The equilibrium point ( λi∗ , N i∗ ) of the closed loop formed by a producer node (5), (3), and (7) with y i = δ i , and the controller (9) is given by λi∗ = δ i for the production rate, and N i∗ = N d ,i for the inventory level. Meanwhile, the closed loop of a non producer node (5), (6), (7), and (10) posses the equilibrium point λi∗ = δ i , N i ∗ = N d ,i , ∗ Oi = ⎡⎣δ iτ i N d ,i + 1 ⎤⎦ / N d ,i . Proof: Follows as the proof of Theorem 1, with the supply chain dynamics equal to zero. ■ Theorem 5 The closed loop system formed by a producer node (5), (3), and (7) with y i = δ i , and the controller (9) is locally asymptotically stable and converge to the equilibrium point λi∗ = δ i , N i∗ = N d ,i if the gains α i , KP ,i , and KI ,i satisfy

(

)

αi > 0

(25)

ζ 3 ≤ KP ,i

(26)

0 < K I ,i < ) ⎡ 1+ e ( ) ⎣ (α λ ( = − αi Nd , i

ζ3

K P , i Wi ( N d , i ) N d ,i (1 + N d ,i )

2

i i ,max

(27)

)

N d ,i ( N d ,i + 1) − 4Wi '( N d ,i )⎤⎦

(

)

−αN ) −1 α iλi ,max N d ,i ( N d ,i + 1)i e ( i

d ,i

2

With Wi '( N d ,i ) as in (19). Furthermore, the above conditions ensure an overdamped closed loop system, avoiding large overshoots and keeping physical and operations constraints given by (4). Proof: Follows as for Theorem 2 with

Wi ( N d , i ) ⎡ ⎢ − N ( N + 1) d ,i d ,i x=⎢ ⎢ 1 ∗ ⎢ T Wi '( N i ) ⎣ i

⎤ 1 ⎥ ⎥x 1⎥ − ⎥ Ti ⎦

(28)

Then by evaluating the system eigenvalues it follows that the closed loop is asymptotically stable and overdamped , if the following condition fulfills

Wi ( N d ,i ) − Wi '( N d ,i ) ≤ 0 N d ,i ( N d ,i + 1)

(29)

The rest of the proof follows as for Theorem 2, yielding the stability and convergence conditions (25), (26) and (27). ■

63


Theorem 6 The closed loop system formed by a non producer node (5), (6) and (7) with the controller (10) is locally asymptotically stable and converge to the equilibrium point λi∗ = δ i ,

N i∗ = N d ,i and Oi∗ = ⎡⎣δ iτ i ( N d ,i + 1) ⎤⎦ / N d ,i , if the gains α i , KP ,i , and KI ,i satisfy

αi > 0 ) ⎡ 1+ e ( ) ⎣ (α λ ( ≤ − αi Nd ,i

K P ,i

2

i i ,max

(30)

(

α iλi ,max N d ,i ( N d ,i + 1) i e K I ,i ≤

)

N d ,i ( N d ,i + 1) − 4λi '( N d ,i )⎤⎦ − (α i N d , i )

)

−1

2

KP ,iφ N d ,i ( N d ,i + 1)

(31)

(32)

φ = 2 τ i N d ,i ⎣⎡ N d ,i 2 (1 − τ i ) + τ iλi ( N d ,i )⎦⎤ + N d ,i 2 (1 − 2τ i ) + λi ( N d ,i ) Furthermore, the above conditions ensure an overdamped closed loop system. Proof: It follows as for Theorem 5.

■

4. Study case application At this section the modelling and control techniques proposed in sections 2 and 3 are applied to a simulation case study. For the linear supply chain a multiproduct petrochemical plant is considered. Meanwhile, for the nonlinear supply chain with orders handling an extraction, blending and distribution system for crude oil is studied. 4.1 Linear supply chain: multiproduct petrochemical plant The proposed modelling and controller techniques for linear supply chains, Sections 2.1 and 3.1, are tested by simulations on a multi-product petrochemical company, which produces different grades of polyethylene products, see Figure 3. The numbers on the left upper side of the nodes identifies the numbering used through the simulations and figures presenting plots of the results. Hexane and catalyst are imported, whereas ethylene is obtained from a local refinery. The production of ethylene and butane is carried out by independent production plants. There exist intermediate storages for the hexane, ethylene, butane and catalyst feedstocks. Only five demand sources are taken into consideration, from D1 to D5. The reactors R1 and R2 produce different polymeric products depending on the fed material (production ratio) and operation conditions. Each reactor produces two polymers: R1 produces A1 and A2, and R2 produces B1 and B2 in a cyclic way, according to a given schedule; R1, R2 and their storages are of multi-product kind, thus have different stocks per product. For supply chain synchronization, it is considered that the demanded product is supplied to the costumer only during the production time of the corresponding product.

64


Fig. 3. Supply chain for a multi-product polyethylene petrochemical plant The simulated period is 10 hrs. Reactors R1 and R2 change from producing product A1 to A2 and B2 to B1 at t=5 hrs respectively. The production ratios and demands for the four products are listed in Table 1. The rest of the production ratios are independent of the kind of product, such that F8,9 = 1.5 and F9,11 = 0.8 , and those for storage purposes are all equal to 1. The plant capacity per reactor is 34.24 [MT/hr]. For reactor R1 and product A1 a desired inventory level of N d ,3 = 400 is considered, while for product A2, N d ,3 = 395 . For the distributor of products A1 and A2, N d ,4 = 440 for A1 and N d ,4 = 420 for A2. Similarly for reactor R2 and its distributor N d ,14 = 420 and N d ,15 = 1000 for B1, N d ,14 = 420 and N d ,15 = 1300 for B2. A1

A2

B1

B2

F2 ,3

0.25

0.4

F2,14

0.6

0.4

F10,3

0.15

0.2

F10,14

0.15

0.1

F12,3

0.5

0.3

F12,14

0.1

0.3

F13,3

0.2 5 3 12

0.1 8 6 11

F13,14

0.15 9 13

0.2 14 8

D1 D2

D3

D4 D5

Table 1. Demands in [MT/hr] and production ratios for products A1, A2, B1 and B2. The storage capacity of the plant is of 2000 [MT] for nodes 2, 10, and 12; 10000 [MT] for nodes 4, 13 and 15; and for nodes 3, 8, 9, 11 and 14 of 500 [MT]. According to a monthly schedule with daily resolution, inventory levels on nodes 1 and 13 must be of 3000 [MT] and 2500 [MT], respectively. The desired inventory levels for the rest of the nodes are listed in Table 2. Note that the planning or scheduling instance must take into account the physical and operational limitations listed in Table 2 to provide feasible and attainable inventories.

65


The initial values at t=0 hrs and the operational limitations are listed in Table 2. For the multi-product reactor R1 its initial values are for product A1 N 3 (0) = 405 , while for product A2, N 3 (0) = 390 . For the distributor the initial values are N 4 (0) = 435 for A1 and

N 4 (0) = 425 for A2. Similar for R2 and its distributor the initial values are N 14 (0) = 385 and N 15 (0) = 990 for B1, N 14 (0) = 415 and N 15 (0) = 1310 for B2. Node

1

2

N i (0)

3000

992

3

4

8

9

10

11

12

13

377

377

360

356

360

2500

14

λi (0)

24

36

39

33

Ti

0.5 34.24

0.1 50

0.1 60

50

0.2 55

120

60

370

380

365

360

365

2500

λi ,max

120

N d ,i

1000

40

15

22.8 0.1 34.24

60

Table 2. Initial values Ni(0), λi(0), maximum production and incoming rates λi,max [MT/hr], desired inventories Nd,i [MT], and adaptation time Ti [hr]. Note that the initial values for the inventories are near to the desired ones as to generate illustrative curves with small oscillations and fast convergence. Nevertheless the controller can deal with large differences on the initial inventories and productions rates with respect to the desired ones. The bounds for the control gains were calculated according to Theorems 2 and 3, such that the gain values (Table 3) were chosen inside the corresponding bounds. Node αi

2

3

4

8

9

10

11

12

14

15

1

2

10

1

2

1

1

1

5

1

KP , i

0.1

0.9

1

0.5

0.3

0.1

0.2

0.1

0.6

0.1

KI ,i

0.07

0.08

0.07

0.1

0.04

0.07

0.04

0.07

0.04

0.07

Table 3. Control gain values Figure 4 presents the inventory levels for producer N 3 , N 9 , and non producer N 4 , N 10 nodes. Note that all inventories converge to their desired values with smooth response. The inventory N 9 shows higher oscillations than the others during transient (t < 1) because it is the node most to the left of the shown ones, such that it is affected by the dynamic changes of all the related downstream nodes. This is the phenomena that origins the bullwhip effect in large supply chains. Notice that the inventories N 3 , N 4 implies individual stock levels for the products A1 and A2. From Figures 5 and 6 notice that all rates touch their boundaries at transient and when changes in production from A1 to A2 are required (t=5 hrs), which shows that the physical and operational bounds, equation (4), are held. As a result of changing the production from A1 to A2, λ3 changes its value, while the inventories of the products A1 and A2 converge to their desired values, see Figure 4. The producer λ9 , λ11 , and incoming λ10 rates are shown in Figure 6, where it is shown that λ9 synchronizes to its demanding rates, with F9,10 = 1 and F9,11 = 0.8 . Also notice that λ3 , and λ4 synchronizes between them accordingly

66


to the production ratio F3,4 = 1 , see Figure 5. Meanwhile the incoming rate λ4 synchronizes to the total demand of product A1 of 20 [MT/hr] and A2 of 25 [MT/hr], such that instantaneous consumption synchronization is achieved.

Fig. 4. Inventory levels N 3 , N 4 , N 9 , and N 10 .

Fig. 5. Production λ3 and incoming λ4 rates. Figure 7 shows a comparison study for different control gains. Note that although the PI control action is filtered by the bounded function, see equations (9), (10) and (11), the behaviour of the PI actions is preserved. A bigger overshoot but faster convergence is obtained when increasing the proportional action and a smaller stationary error is achieved by increasing the integral action.


67

Fig. 6. Production λ9 , λ11 , and incoming λ10 rates.

Fig. 7. Comparison study for different gain values. 4.2 Nonlinear supply chain: crude oil blending and distribution system The modelling techniques and controller proposed in Section 2.2 and 3.2 for nonlinear supply chains that consider orders handling are tested by simulations on a blending and distribution crude oil system. The goal is to keep a desired inventory level, while the demanding rate at the distribution side must be satisfied. Figure 8 shows the crude oil supply chain system, where the numbers on the left side identifies the nodes. The initial and desired inventory values in [MT] are listed at Table 4 as well as the adaptation time Ti for producers and delay time for processing orders i in [hr].

68


Fig. 8. Extraction, blending and distribution system for crude oil.

τi

δi

λi ,max

Node

N d ,i

N i (0)

1

8000

9000

400

2

6000

7000

400

3

2000

4000

Ti

2

400

4

3000

4000

0.25

5

1500

2000

0.25

150

200

500

6

1000

2000

0.25

200

250

Table 4. Initial values and desired inventories. The bounds for the control gains were calculated accordingly to Theorems 5 and 6 such that the gain values (Table 5) were chosen inside the corresponding bounds. Node αi

1

2

3

4

5

6

1

0.1

1

1

2

1

KP , i

0.97

0.6

2.02

0.11

0.14

0.11

KI ,i

3x10-6

3x10-6

0.25

0.22

0.28

0.22

Table 5. Control gain values Figures 9 and 10 show the results obtained at the closed loop, notice that the inventory levels converge to their desired values, while the control actions follow the demands, thus demands are satisfied. The demand and control actions show the delay introduced by considering traffic flow theory at the dynamic models, nonetheless at steady state they synchronizes each other. Several conditions, such as noise at demands and sudden changes on desired inventories, have been tested by simulations, showing robustness of the closed loop system; however for the brevity of space the plots are skipped. The oscillations during transients at demands and control actions are greatly affected by the delays modelled by Ti and i, such that bullwhip phenomena can arise.

5. Conclusions and trends Supply chains are challenging systems due to their dynamical behavior, ranging from fast changing demand, transport and delivery delays, inventory management, and production


69

Fig. 9. Supplier nodes 1 and 2. phenomena. Appropriate control and operation of such systems may contribute to achieve the enterprise objectives. In this work, the proposed supply chain models go from a simple linear supply chain based on material flows to a nonlinear one considering orders handling and based on traffic flow theory. Nonetheless the simplicity of both proposed models, they reproduced production and transport delays by introduction of time parameters. However, there are several supply chain phenomena that are still to be modeled, such as recycling material loops, hybrid systems with continuous and discrete time dynamics, stochastic behavior, among many others. Differential equation models, as the ones considered here, may be useful on modeling such phenomena, but there are other approaches such as fuzzy sets, neuronal networks, to mention a few, that could be considered. Furthermore, both the model and control of supply chains must consider the physical and operative constraints. For this purpose, bounded control actions have to be proposed. Several trends on control of supply chains go from classic control theory to time discrete systems, fuzzy strategies and even evolutive algorithms. Due to the type of presented supply chain models it is straightforward to consider classic control techniques such as the PI controllers introduced at this work. An important point when proposing control strategies is the capability of determining stability and operative conditions for the system.

70


Fig. 10. Nodes 3, 4, 5, and 6. This is a key issue to proceed for implementation of the controller to a real supply chain system, otherwise, instability or critical operative conditions may arise. Classic control techniques have the advantage of a well developed variety of stability analysis tools, like pole placement and linearization techniques in which this work is based on to obtain tuning guidelines and stability conditions for the closed loop system. Simulation study cases have been considered for the purpose of demonstration of the closed loop performance achieved by the proposed models and controllers. In general the simulations allow concluding stability of the supply chain system, convergence of the inventory levels to the desired values and synchronization of the material flows. Thus everything what was predicted by the stability analysis has been confirmed by the simulation case studies. Although comparison and analysis of simulation results are valid to draw some conclusions, development of scale models or prototypes has to be considered for a better understanding of supply chains and more realistic implementation of the controllers and management strategies of such systems.


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6. References Cetinkaya, S., & Lee, C., (2000). Stock replenishment and shipment scheduling for vendor managed inventory systems, Management Science, 46, 2, electronic ISSN 217-232, 217-232. Daganzo, C.F. (2002). A Theory of Supply Chains, Springer-Verlag, ISBN: 3-540-00288, Berlin, Germany. Dunbar, W.B. & Desa, S. (2005). Distributed Model Predictive Control for Dynamic Supply Chain Management, Int. Workshop on Assessment and Future Directions of NMPC, Freudenstadt-Lauterbad, Germany, August, 26-30. Fujimoto, H. (2002). Supply chain for the synchronization of production and distribution. Osaka Keidai Ronshu, 53, 3, 307-315. Helbing, D., (2003) Modeling supply networks and business cycles as unstable transport phenomena. New Journal of Physics, 5, 90, ISSN 1367-2630, 90.1-90.28. Helbing, D., Lämmer, S., Seidel, T., Seba, P. & Platkowski, T. (2004). Physics, stability, and dynamics of supply chain networks. Physical Review E, 70, 6, 066116(1-6), ISSN: 1550-2376. Koudal, P., (2003). Integrating demand and supply chains in the global automotive industry: building a digital loyalty network at General Motors. Deloitte Research and Stanford Global Supply Chain Management Forum, Research article, www.dc.com/research Koudal, P., & Wei-the ,V. L., (2005). The power of synchronization: the case of TAL Apparel Group, Deloitte Research, Research article, www.dc.com/research Lababidi, H., Ahmed, M., Alatiqi, I., & Al-Enzi, A., (2004). Optimizing the supply chain of a petrochemical company under uncertain operating and economic conditions, Industrial & Engineering Chemistry Research, 43, 1, ISSN: 0888-5885, 63-73. Lefeber, E., (2004). Nonlinear models for control of manufacturing systems, Nonlinear dynamics of production of production systems, Ed. G. Radons and R. Neugebauer, ISBN: 3-527-40430-9, 71-83. Lin, P., Wong, D., Jang, S., Shieh, S., & Chu, J., (2004). Controller design and reduction of bullwhip effect for a model supply chain system using z-transform analysis, Journal of Process Control, 14, 5, ISSN: 0959-1524, 487-499. Nagatani T. & Helbing, D. (2004). Stability analysis and stabilization strategies for linear supply chains. Physica A, 335, 3-4, 644-660, ISSN: 0378-4371. Perea-Lopez, E.; Grossman, I.E., Ydstie, B.E. & Tahmassebi, T. (2001). Dynamic Modeling and Decentralizes Control of Supply Chain. Ind. Eng. Chems. Res., 40, 15, 3369-3383, ISSN: 1520-5045. Perea-Lopez, E., Ydstie, B.E. & Grossman, I.E.; (2003). A model predictive control strategy for supply chain optimization. Comp. & Chem. Eng., 27, 15, 1201-1218, ISSN: 00981354. Petrovic, D., Roy, R., & Petrovic, R., (1999). Supply chain modeling using fuzzy sets, International Journal of Production Economics, 59, 1-3, ISSN: 0925-5273, 443-453. Shapiro, J. F. (2007). Modeling the Supply Chain. Duxbury Thomson Learning Onc., ISBN: 0495-12611-x, h., Pacific Belmont CA.

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Steidmann, C. (2004). Synchronicity: an emerging vision of the retail future. Deloitte Research, Research Brief Report, May, www.dc.com/research. Tayur, S., Ganeshan, R., & Magazine, M., (1998). Quantitative models for supply chain management, Boston, Kluwer Academic Publishers, ISBN: 0-7923-8344-3.

5 Optimization Based e-Sourcing Kameshwaran Sampath1 and Lyès Benyoucef2 1Indian

2INRIA

School of Business Nancy-Grand Est 1India 2France

1. Introduction Sourcing or procurement is the process by which a company obtains goods and services for its manufacturing and operations. The materials procured could range from raw materials, components, and sub-assemblies, to office supplies and furniture. The services procured can be as vital as design and R&D to daily operations like IT and logistics. e-Sourcing or eprocurement refers to online procurement of the above direct and indirect inputs by an industrial buyer. The other predominantly used terms for this process are procurement auctions, reverse auctions, and e-auctions. We use the above terms in this chapter with the following definition of Minahan (2001): the process of utilising Web-based technologies to support the identification, evaluation, negotiation, and configuration of optimal groupings of trading partners into a supply chain network, which can then respond to changing market demands with greater efficiency. e-Sourcing of production and non-production goods and services has been in practice since early 1990s, especially among the Fortune 2000 companies. It was widely accepted then that web based sourcing can provide following advantages (Minahan 2001): Identify and negotiate with a broad range of qualified suppliers; Reduce process costs for sourcing engagements; Shorten sourcing cycles by 25% to 30%; Reduce time-to-market cycles by 10% to 15%; Negotiate an average of 5% to 20% unit price reductions; Extend strategic sourcing to a wider range of products and services; and Enhance collaboration and knowledge sharing. The market analysts’ predictions about the worth of online business transactions were in trillions of USD by 2003/4. However, the e-bust that happened in 2000, followed by the market studies of the real world implementations showed that these figures are indeed exaggerated and overstated, if not false (Emiliani & Stec, 2004; 2005). Irrespective of the figures projected and achieved, the use of e-sourcing is growing with steady incremental gains rather than abrupt exponential profits. Ariba1, a leading provider of online spend management solutions, has enabled sourcing of USD 250 Billion worth of goods and services till date, using its Ariba Sourcing Solution. The total annual savings generated is over USD 15 Billion (Ariba, 2007). 1

http://www.freemarkets.com

74 -


GlaxoSmithKline achieved a 5,452% annualized return-on-investment using Emptoris2 (another leader in providing sourcing solutions and a pioneer in the use of optimization for strategic sourcing in industry). Motorola received the prestigious Franz Edelman Award for Achievement in Operations Research and the Management Sciences in 2004 for the application of optimization bid analysis with Emptoris to save USD 600 million. More than 60 Fortune 500 companies use CombineNet3 for their most advanced strategic sourcing activities, with greater than 45x average return on investment. Volume-discount and combinatorial auctions benefited Mars Inc. and its suppliers (Hohner et al., 2003). Chilean government used combinatorial auctions to assign catering contracts for the supply of school meals to children and resulted in a 22% cost savings (Epstein et al., 2002). In this chapter, we present different optimization based e-sourcing auctions from the literature and industry best practices. We also extend how these mechanisms could be used in global sourcing and the future research to include risk mitigation. Auction is a market mechanism with well-defined set of rules for determining the terms of an exchange of something for money (McAfee & McMillan, 1987). Procurement auctions are reverse auctions in the sense that the buyer is the auctioneer and the sellers (suppliers) are the bidders. Traditionally, auctions for procurement at the industrial scale were mainly used by government for purchasing goods and services. The main reasons are openness and fairness of auctions, and till today, government purchasing happens through auctions. However, for many years, auctions played a relatively minor role in industrial procurement (Rothkopf & Whinston, 2007). The industrial approach to procurement was to develop long-term cooperative relationships with few suppliers. The advent of Internet and the advancement of e-commerce changed this approach radically. The long term strategic partnerships with suppliers are still prevalent for sourcing of custom designed goods and services. On the other hand, for sourcing of commoditized goods and services, e-sourcing through auctions are being increasingly used by industries (Kouvelis et al., 2006). Also among the academics, there is a recent growing interest in this Internet based business process that led to new generation of procurement techniques like combinatorial (Cramton et al., 2006), volume discount (Eso et al., 2005) and multi-attribute (Bichler et al., 1999; Kameshwaran et al., 2007). e-Sourcing and in general, e-auctions, are being studied by scholars from different disciplines such as economics, operations research, management science, information systems, and computer science (Rothkopf & Whinston 2007). Many works from operations research and management science community approach e-sourcing from the perspective of solving a supply chain optimization problem. This chapter also adopts the same perspective and other economic issues like information asymmetry, adverse selection, and moral hazards (McAfee & McMillan, 1987) are not addressed here. The chapter is organized as follows. Section 2 describes the dynamics of the sourcing process, briefly outlines the design issues, and introduces the three different sourcing formats considered in this chapter. The three sourcing techniques are presented in detail in 2 3

http://www.emptoris.com http://www.combinenet.com

Optimization Based e-Sourcing

75

sections 3 to 5. For each of the techniques, the mathematical programming formulation is presented. Sourcing based on volume discounts, one of earliest formats in business tradition, is presented in Section 3. The more recent and popular combinatorial sourcing is described in Section 4. Multi-attribute sourcing and its extension configurable bids are discussed in Section 5. Section 6 is devoted to global sourcing, where the traditional industrial procurement format of a single buyer with multiple suppliers is extended to multiple factories of the same organization procuring from multiple suppliers. Section 7 discusses the robustness approach to e-sourcing to design a risk-tolerant sourcing network that will operate at acceptable levels under a wide range of pre-identified random scenarios. Final notes are given in Section 8 and references are listed in Section 9.

2. Dynamics, design issues, and taxonomy 2.1 Dynamics The overall industrial sourcing dynamics can be described as a standard three-step process: (1) Pre-auction stage, (2) auction stage, and (3) post-auction stage. This is adapted from the three-step process described in Caplice & Sheffi (2007) for procurement of transportation services. Pre-auction stage The buyer forecasts the demand for the planning horizon and determines which suppliers to invite for the sourcing auction. Common practice is to retain most incumbents (to maintain long term buyer-supplier relationship) and invite some new suppliers. A set of mandatory supplier selection criteria (Weber et al. 1991) is used to identify these potential suppliers. The buyer also decides the format of the auction and bid structure (to be described in detail). Auction stage The demand, auction format, and the bid structure are communicated to the pre-selected suppliers in the form of RFQ or RFP through the use of faxed lists, spreadsheets, online web pages, email, or direct EDI connections. The suppliers conduct their own analysis and prepare the bid according the required format. The bidding phase in the auction could be single round or multiple rounds (see Figure 1). Once the bids are received, the buyer solves the winner determination problem (WDP), where the bids are evaluated to determine the winning the suppliers (also known as bid evaluation problem). In the single round auction, the auction closes after this stage and the suppliers are intimated of their status. In multi-round auctions, the winners determined are provisional winners and the suppliers are given feedback information, using which they can resubmit bids. Once a termination criterion is met the auction is closed. The criterion could be that no new bids from suppliers or the upper bound on the number of rounds reached. Post-auction stage Once the auction is closed, the results of the WDP are uploaded to the downstream planning, execution, auditing, and payment systems. 2.2 Design issues The sourcing process with RFX generation and bidding by suppliers is inherently based on auctions and hence the design principles for sourcing generally follow auction design. As mentioned above, auctions can be categorized based on the dynamics as: (1) single-round or one-shot auctions and (2) multiple-round or progressive or iterative auctions. Single-round auctions are sealed bid auctions. Multi-round auctions can be sealed bid or open bid, but has

76

Supply Chain, The Way to Flat Organisation Buyer starts auction

Suppliers submit bids

Buyer starts auction

Suppliers submit bids

Buyer evaluates Bids: WDP Buyer evaluates Bids: WDP Feedback information to suppliers Buyer closes auction

Termination criterion met?

No

Yes Buyer closes auction

(a) Single-round auction

(b) Multi-round auction

Fig. 1. Single-round and multi-round auction dynamics multiple rounds of bidding phases. At the end of each bidding phase, there will be flow of information from the auctioneer to the bidders. This will help the bidders to prepare their bids for the next bidding phase. The design parameters of single-round auctions are bid structure, winner determination policy, and pricing policy. The bid structure specifies the format of bids, the winner determination policy describes the technique to determine the winners, and the pricing policy determines the price(s) of the winning good(s). On the other hand, design of multi-round auctions is relatively non-trivial, which includes the specification of bid structure, winner determination technique at each bidding round, information exchange at the end of each round, termination condition, and the pricing policy. Multi-round auction has many advantages over its one-shot counterpart (Cramton 1998), especially in sourcing (Parkes & Kalagnanam 2005). There are many design methodologies for multi-round auctions for sourcing (Bikhchandani & Ostroy 2006, Kameshwaran et al. 2005, Parkes & Kalagnanam 2005). The sourcing process with the RFX and the bidding, only borders on auctions and are indeed less formally structured than auctions. The auction design is generally based on the


77

principles of mechanism design. Mechanism design (Mas-Colell et al., 1995) is the sub-field of microeconomics and game theory that considers how to implement good system-wide solutions to problems that involve multiple self interested agents, each with private information about their preferences. The mechanism design methodology has also been found useful in designing e-markets (Varian, 1995). One of the main assumptions in mechanism design is that the rules of the auction are a common knowledge to all the participating agents. In sourcing, though the rules of bid submission are common knowledge, rules of winner determination may not be revealed to the suppliers. The purchasing manager may take into account several business rules and purchasing logic in winner determination, which are not generally revealed to the suppliers. Moreover, the criteria and the constraints can be modified by the auctioneer (buyer), based on the received bids. Here, we do not follow the mechanism design approach. Pricing policy is another design issue, which dictates the price to be paid to the winning suppliers for the supply of the winning goods. The commonly used pricing policy in current e-sourcing systems is the pay-as-bid or first price policy where the suppliers are paid the cost quoted in their respective bids. There are non-trivial pricing policies such as VCG (Ausubel & Milgrom, 2006), where the price is function of the price quoted by the other suppliers. Though this pricing policy has certain desirable economic features, it is not widely used in practice.

Fig. 2. Factors considered by Motorola in awarding business to suppliers (Source: Metty et al. (2005)) 2.2 Bid structure and winner determination We consider only the design issues related to bid structure and winner determination technique, from the perspective of the buying organization (auctioneer). The bid structure dictates how a bid is defined. For example, it could be as simple as a unit price for an item or set of attributes like unit price, lead time, quantity, etc. A rich bid structure is advantageous

78


to both the buyer and suppliers. The buyer has more negotiating parameters rather than just unit price and hence can optimize the total cost or procurement. Suppliers, on the other hand, can differentiate themselves from their competitors with value added services rather than competing on just cost. The earlier e-sourcing techniques achieved cost reduction to buyers by squeezing the profit margins of the competing suppliers. Many historical and incumbent suppliers did not prefer the online sourcing as they felt that the buyers used it to wring price concessions from them in the presence of new suppliers (Jap, 2002). However, esourcing evolved with a rich set of bid structures, providing a win-win situation to both the buyers and sellers, and thereby achieving overall supply chain efficiency. Figure 2 shows the factors considered by Motorola to minimize the total cost of ownership while awarding business to the suppliers (Metty et al., 2005). The winner determination problem faced by the buyer at the end of the bidding phase (or at the end of every bidding phase in progressive auctions) is an optimization problem. The problem is to determine the set of winning suppliers and their respective winning items, such that the total procurement cost is minimized subject to various business constraints and purchasing policies. Indeed, one of the earliest applications of linear programming is winner determination (also referred as bid evaluation) (Gainen et al., 1954). Many commercial bid analysis products from companies like Emptoris, and CombineNet use optimization techniques like linear programming, combinatorial optimization, and constraint programming. Optimization also allows for addition of business constraints and purchasing logic as side constraints in winner determination, which is a new development in sourcing auctions (Rothkopf & Whinston, 2007). Some of the commonly used business rules are: Limiting the number of winning suppliers in a given range; Limiting the business awarded (in terms of quantity or worth) to a winning supplier in a given range; Guaranteeing a minimum amount of business to incumbent suppliers; Note that the above business rules need not be disclosed to the suppliers and often many of them are experimented with WDP like analyzing what-if scenarios. 2.3 Taxonomy We categorize the e-sourcing techniques based on the bid structure and the winner determination policy, which also implicitly depends on the number of goods purchased and their respective quantities. We broadly classify e-sourcing under three categories: (a) Volume discount sourcing, (b) Combinatorial sourcing, and (c) Multi-attribute and multicriteria sourcing. We describe each of the above in detail in the following sections.

3. Volume discount sourcing Volume or quantity discounts in sourcing is a long established business tradition. Buyers expect discounts for buying large quantities and the suppliers provide discounts to price discriminate from the competing suppliers. Studies by Lippman (1969), Prikul & Aras (1985), Jucker & Rosenblatt (1985), and Dolan (1987) focus on how buyers determine the economic order quantities with quantity discounts. On the other hand, Crowther (1964), Monahan (1984), Lee & Rosenblatt (1986), and Kim & Hwang (1988) focus on the supplier's perspective of formulating the form of quantity discount pricing schedule.


79

Fig. 3. Piecewise linear cost function as bid According to Sadrian & Yoon (1994), the rationale behind quantity discount models is derived from the numerous economic advantages gained from buyers ordering larger quantities of products. With larger orders, both the supplier and the buyer should be able to reduce their order processing costs. These might include packaging and handling costs, administrative costs, and shipping costs. However, the major benefits come from the savings in the supplier's manufacturing costs. A supplier that produces the item itself for these larger orders will require fewer manufacturing setups and have larger production runs. These savings are especially significant if the supplier experiences high product-specific setup costs. On the other hand, increased order size results in a higher inventory holding cost for the buyer. Therefore, the supplier should compensate for this extra cost with an attractive quantity discount pricing schedule to induce buyers to increase their order size. Munson and Rosenblatt (1998) provided a classification of different quantity discounts. The form of discount could be all-units or incremental. In all-units form, the discount is applicable to all the units purchased, whereas in incremental, the discount is applicable only to the additional units that has crossed the break-up point. The item aggregation describes whether the discount applies to one or multiple products (bundling). A business volume discount represents item aggregation where the price breakpoints are based on the total dollar volume of business across all products purchased from the supplier. The time aggregation describes whether the discounts apply to individual purchases or multiple purchases over a given time frame. Finally, the number of price breakpoints may be one, multiple, or infinite (as represented by a continuous price schedule). In this section, we present incremental discount form applied to sourcing of multiple units of a single item (Kameshwaran & Narahari, 2009b). Our focus here is on solving the WDP faced by the buyer, when the suppliers submit non-convex piecewise linear quantity discount price functions. 3.1 Sourcing of multiple units of a single item Consider an industrial procurement of multiple units of a single item. The demanded item can be a raw material or a sub-component and let the demanded number of units be B. Let N

80


be the number of potential suppliers, out of whom the winning suppliers have to be selected. Usually, the buyer prefers multi-sourcing as the supply failure in a single supplier scenario will be disruptive. Further, the demand B could be formidably large for a single supplier. With multi-sourcing, the buyer needs to determine the winning suppliers and also their winning quantity. This provides an additional negotiable parameter to the suppliers in addition to unit price. The bid thus is a price function defined over quantity. The price function that is commonly used in industry for long-term strategic sourcing is piecewise linear (Davenport & Kalagnanam, 2002). 3.2 Piecewise linear bid The bid submitted by supplier j is a cost function Q j defined over the supply quantity [ a j , z j ]. Q j is piecewise linear and nonconvex, as shown in Figure 3. It can be represented compactly by tuples of break points, slopes, and costs at break points: l l l For notational convenience, define Q j ≡ ((δ j0 = a j ,..., δ j = z j ),( β 1j ,..., β j ),(n 0j ,..., n j )). ~ ~ δ js ≡ δ js − δ js − 1 and n sj as the jump cost associated with linear segment s . Note that, by this definition, n 0 = ~ n0 . j

j

Notations [a j , z j ] Qj lj

β js δ js n sj ~ n sj

Qj

j

j

j

Supply range Cost function defined over [ a j , z j ] Number of piecewise linear segments in Q j ~ ~ Slope of Q j on (δ js −1 , δ js ) ~ ~ ≡ δ js − δ js − 1 Fixed cost associated with segment s ~ Q j (δ js ) + nsj ~ ~l l ~0 ,..., n ~l j )) ≡ ((δ j0 = a j ,..., δ j j = z j ), ( β j1 ,..., β j j ), (n j j

The function is assumed to be strictly increasing, but not necessarily marginally decreasing as shown in the figure. The assumed cost structure is generic enough to include various special cases: concave, convex, continuous, and aj = 0. The cost structure enables the suppliers to express their volume discount or economies of scales and/or the production and logistics constraints. The volume discount strategy, which is buy more and pay less can be expressed with marginally decreasing cost functions. The discontinuities with jump costs in the cost structure can capture the production and transportation constraints. 3.3 Winner determination problem Let J be the set of N suppliers. The index j denotes a supplier from set J. As each supplier can only submit one bid, we use the index j to denote both the supplier and his bid. The winner determination problem (WDP) faced by the buyer is to minimize the total cost of procurement with the following decisions: (1) select a set of winning bidders J ′ ⊆ J and (2) determine the trading quantity q j for each winning bid j ∈ J ′. The above decisions are to be

made subject to the following constraints:

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-

Supply Constraint: For every winning bid j ∈ J ′ , q j ∈ [ a j , z j ] , and for losing bids, q j = 0.

-

Demand Constraint: The total quantity procured should satisfy the demand of the buyer: ∑ q j ≥ B. j∈J ′

The WDP is a nonconvex piecewise linear knapsack problem (Kameshwaran & Narahari, 2009a), which is NP-hard. It is a minimization version of a nonlinear knapsack problem with a demand of B units. Each bid corresponds to an item in the knapsack. Unlike traditional knapsack problems, each item j can be included in the knapsack in a pre-specified range [ a j , z j ] and the cost Q j is a function of quantity included. The cost function Q j of Figure 3 is nonlinear but due to the piecewise linear nature, the WDP can be modelled as the following MILP. lj ⎛ min ∑ ⎜ n 0j d 0j + ∑ n sj d sj + β jsδ js x sj ⎜ j∈J ⎝ s =1

(

)⎞⎟⎟ ⎠

(1)

subject to d 1j ≤ d 0j ∀j ∈ J

(2)

x sj ≤ d sj ∀j ∈ J ; 1 ≤ s ≤ l j

(3)

x sj ≥ d sj + 1 ∀j ∈ J ; 1 ≤ s < l j

(4)

⎛

lj

⎞

s =1

⎠

∑ ⎜⎜ a j d 0j + ∑ δ js x sj ⎟⎟ ≥ B j∈J ⎝

(5)

d sj ∈ {0 ,1}

∀j ∈ J ; 0 ≤ s ≤ l j

(6)

x sj ∈ [0 ,1]

∀j ∈ J ; 1 ≤ s ≤ l j

(7)

The decision variable x sj denotes the fraction of goods chosen from the linear segment s of bid j. For this setup to make sense, whenever x sj > 0 then x sj − 1 = 0 , for all s. To enable this, binary decision variable d sj is used for each segment to denote the selection or rejection of l segment s of bid j. The winning quantity for bid j is a j d 0j + ∑sj=1 δ js x sj with cost lj 0 s s s s s 0 0 n j d j + ∑s=1 n j d j + β j δ j x j . The binary decision variable d j is also used as an indicator variable for selecting or rejecting bid j , as d 0j = 0 implies that no quantity is selected for trading from bid j.

(

)

3.4 Business constraints The business rules and purchasing logic can be added as side constraints to the WDP. For the above procurement scenario, the relevant business constraints are restricting the number of winning suppliers in a given range [LB, UB] and guaranteeing a minimum volume (or

monetary business worth) MIN_QTY (MIN_VAL) for a set of incumbent suppliers J ' ⊂ J .

82


LB ≤ ∑ d 0j ≤ UB

(8)

j∈J

⎛

lj

⎞

s =1

⎠

∑ ⎜⎜ a j d 0j + ∑ δ js x sj ⎟⎟ ≥ MIN _ QTY j∈J '⎝

⎛

lj

⎞

s =1

⎠

∑ ⎜⎜ n 0j d 0j + ∑ (n sj d sj + β jsδ js x sj )⎟⎟ ≥ MIN _ VAL j∈J '⎝

(9)

(10)

The above constraints can be added as side constraints to the WDP. Usually one of the (9) or (10) is used. Business rule that limits the winning quantity or business value for a winning supplier can be implicitly included by suitably modifying the supply range [ a j , z j ] . 3.5 Algorithms Dynamic programmic based exact and approximation algorithms were proposed in (Kameshwaran & Narahari, 2009a) and a Benders’ decomposition based exact algorithm was proposed in (Kameshwaran & Narahari, 2009b) to solve the WDP formulated as (1)-(7). Similar procurement scenarios have been considered in the literature with various assumptions. Kothari et al. (2003) expressed the cost function using fixed unit prices over intervals of quantities (piecewise linear but continuous with no jump costs) and approximation algorithms based on dynamic programming were developed for solving the WDP. Procurement with nonconvex piecewise linear cost functions was considered by Kameshwaran & Narahari (2005) with the additional business constraint of restricting the number of winning suppliers. A Lagrangian based heuristic was proposed to solve the WDP. Eso et al. (2005) considered the quantity discount procurement of heterogeneous goods and column-generation based heuristic was proposed to solve the WDP. 3.6 Other discount based sourcing techniques In the above, we briefly discussed about volume discounts offered while procuring multiple units of a single item. Eso et al. (2005) considered buying multiple items with volume discounts for each item. There are two kinds of discounts for procuring multiple units of multiple items: Business volume discounts (Sadrain & Yoon ,1994) and total quantity discounts (Goossens et al. 2007). In the business volume discounts, the discounts are based on the total monetary worth of the purchase rather than on the quantity. This discount structure is applicable in telecommunication sourcing. In total quantity discounts, discount is based on the total quantity of all items purchased. This discount is used in chemical and also in telecom capacity sourcing. Exact algorithms based on brand and bound were proposed in (Goossens et al., 2007) to solve this problem. For a special case with single unit demand for multiple items, a suite of branch-and-cut algorithms was proposed in (Kameshwaran et al., 2007).

4. Combinatorial sourcing Consider a sourcing scenario where the buyer wants to buy a set of heterogeneous items. Two immediate approaches to procure them are in sequence (sequential procurement with one after another) and in parallel (all items are procured simultaneously by conducting a

83


sourcing auction for each item separately). The third option is to conduct a combinatorial auction where the supplier can bid on a combination of items by providing a single bid price (Cramton, 2006). Thus the bid price is conditional on winning the entire combination of items. These auctions are ideal for scenarios in which synergies exist between the items. Suppose a supplier obtains more profit by selling a set of items together, then he can submit this all-or-nothing combinatorial bid by providing a discounted price on that entire package. The supplier can submit more than one bid and the items in different bids can be overlapping. Combinatorial auctions were initially used in selling scenarios like airport slot allocation (Rassenti et al., 1982) and radio spectrum auctions (Rothkopf et al., 1998). The sourcing applications mainly include procurement of transportation services (Caplice & Sheffi, 2006), in addition to direct sourcing of industrial inputs (Hohner et al., 2003). In this following, we present various combinatorial bids and the respective WDP formulations. 4.1 Static package bids Let the items to be procured be indexed by i, each with demand di. A bidder j bids on a package or bundle of items, providing a single bid price for that bundle. Let the package be indexed by k. As mentioned above, the bidder can submit different packages as bids with possibly overlapping items. The winner determination problem can be formulated as the following 0-1 integer program.

min

∑∑ C j

k j

y kj

(11)

k

subject to

∑∑δ

k ij

y kj = di ∀i

(12)

j k :i∈k

y kj ∈ {0,1} ∀j, k

(13)

where the notations are: Indices i Item identification j Supplier identification k Package identification Decision variables

y kj

= 1 if supplier j is assigned package k = 0, otherwise

Data

C kj

Bid price for package k of supplier j

δ ijk

Volume of item i as a part of package k for supplier j

The objective function (11) minimizes the total procurement cost. The constraint (12) enforces the demand requirements of the buyer. The above formulation allows for each supplier to win more than one package bids. This is OR bidding language (implying logical OR). Another popular bidding language used in practice is XOR, which allows at most one

84


winning package bid for each supplier. For a more detailed discussion about the bidding languages, see Nisan (2000). The XOR constraint can be easily included as follows:

∑y

k j

≤ 1 ∀j

(14)

k

The above formulation is more appropriate for unit demand di=1 for each item i (hence δ kji = 1 ). For multi-unit demands, flexible package bids are beneficial, as the buyer can choose the winning quantity for each supplier. 4.2 Flexible package bids With flexible package bids, supplier j can provide supply range [ LB kji ,UB kji ] for item i as a part of package k. The formulation for the WDP is as follows:

min

∑∑∑ C j

k

k k ij xij

(15)

∀i

(16)

i

subject to

∑∑x

k ij

= di

j k :i∈k

LB kji y kj ≤ xijk ≤ UB kji y kj

∀i , k , j

(17)

y kj ∈ {0,1} ∀j , k

(18)

x kji ≥ 0

(19)

∀i , k , j

where the additional decision variable and data are: Decision variable that denotes the winning quantity for item i from package k of xijk supplier j

Cijk

Unit bid price for item i from package k of supplier j

4.3 Business constraints Several business rules are used in combinatorial sourcing. We will need additional decision variables and data to add the business rules as side constraints to the WDP. Additional decision variables

wij

= 1 if supplier j supplies item i, = 0 otherwise

zj

= 1 if supplier j is a winning supplier, = 0 otherwise

Additional data Li

[S' , S'' ]

Item limit of suppliers who can supply item i

Range of number of overall winning suppliers

85


[Min_Vol, Max_Vol] [Min_Val, Max_Val] M

Minimum and maximum volume guarantee Minimum and maximum business guarantee A large constant

Fj

Fixed cost of developing supplier j

F ji

Fixed cost of developing supplier j for item i

To limit the number of suppliers at the item level and at the whole sourcing level, following side constraints can be added:

xijk ≤ M wij

∀i , k , j

(20)

∀k , j

(21)

∀i

(22)

≤ S ''

(23)

wij ∈ {0,1} ∀j , i

(24)

z j ∈ {0,1} ∀j

(25)

y kj ≤ M z j

∑w

i j

≤ Li

j

S'≤

∑z

j

j

Minimum and maximum volume (business) guarantees can be enforced with the following constraints:

Min _ Vol z j ≤

∑∑ x

k ij

k

Min _ Val z j ≤

≤ Max _ Vol z j

∑∑ C

k k ij xij

k

∀j

(26)

i

≤ Max _ Val z j

∀j

(27)

i

Including new suppliers into the sourcing network may incur extra fixed costs. This cost is associated with developing and maintaining a long-term relationship with a new supplier. This is due to the joint technology transfer, engineering, and quality programs with the supplier to enable him to meet the buyer’s business and product and requirements. Sometimes the fixed cost could at product level. The fixed cost business constraints, however, need to be added at the objective function.

min

∑∑ C

k k ij xij

j

k

+

∑∑ F w + ∑ F z ij

j

i

i j

j j

(28)

j

4.4 Algorithms Winner determination problems for combinatorial bids are well studied among the current bid structures. As noted in (Sandholm et al., 2005), three different approaches have been

86


pursued in literature: (1) algorithms that find a provable optimal solution but the computational time dependent on problem instances (Sandholm, 2006), (2) algorithms that are fast with guaranteed computational time but can only find a feasible, not necessarily an optimal solution (Lehmann et al., 2002), and (3) restricting the bundles on which bids can be submitted so that the problem can be solved optimally and provably fast (Rothkopf et al., 1998; Muller, 2006). Combinatorial sourcing are supported and conducted by many commercial providers like CombineNet, Manhattan Associates, JDA, NetExchange, and Trade Extensions.

5. Multi-attribute and multi-criteria sourcing In industrial procurement, several aspects of the supplier performance, such as quality, lead time, delivery probability, etc have to be addressed, in addition to the qualitative attributes of the procured item. A multi-attribute bid has several dimensions and this also allows the suppliers to differentiate themselves, instead of competing only on cost. Multi-attribute auctions deal with trading of items which are defined by multiple attributes. They are considered to play significant role in the commerce conducted over the WWW (Teich et al., 1999; Bichler, 2001). A multi-attribute auction as a model for procurement within the supply chain was studied in (Che, 1993). It is a one-shot auction in which the suppliers respond to the scoring function provided by the buyer. Multi-attribute auction for procurement proposed in (Branco, 1997) has two stages: A supplier is chosen in the first stage and the buyer bargains with the chosen supplier in the second stage to adjust the level of quality. The other approach in designing multi-attribute auctions is combining multi-criteria decision analysis and single-sided auction mechanisms. 5.1 Scoring function Evaluating the bids by taking into account different factors is a multi-criteria decision making (MCDM) problem. MCDM has two parts: multi-attribute decision analysis and multiple criteria optimization. Multi-attribute decision analysis techniques are often applicable to problems with a small number of alternatives that are to be ordered according to different attributes. Two commonly used multi-attribute decision techniques (Belton 1986) are multiattribute utility/value theory (MAUT) (Keeney & Raiffa, 1976) and the analytic hierarchy process (AHP) (Saaty, 1980). They use different techniques to elicit the scores or weights, which denote the relative importance among the attributes. MAUT allows one to directly state the scores or estimate as a utility function identified through risk lotteries. AHP uses paired comparisons of hierarchical attributes to derive weights as ratio-scale measures. An insightful comparison of both techniques is presented in (Belton 1986). For a comprehensive study of different multi-attribute decision analysis techniques the reader is referred to (Olson 1996). Multi-attribute decision analysis has been used in traditional supplier/vendor selection problems (Ghodsypour & O’Brien, 1998; Benyoucef et al., 2003). Multi-attribute auction based on MAUT for e-procurement was proposed in (Bichler et al., 1999). The bids submitted by the suppliers are in the form of (attribute, value) pairs. Each attribute has a set of possible values. Thus a bid is an ordered tuple of attribute values. Indices i Attribute identification


87

Ki Set of possible values for attribute i j Supplier identification Multi-attribute bid from j Vj (v1j, …, vij, …) where v ij ∈ K i The buyer assigns weights to the attributes indicating their relative importance and has a scoring function for each attribute. The scoring functions essentially convert each attribute value to a virtual currency, so that all attribute values can be combined into a single numerical value that quantifies the bid. The combination rule generally used is the weighted additive combination. Scores and weights Si Scores for values of attribute i: Si ( vij ) ∈ R wi Weight for attribute i Additive scoring function for bid Vj ∑ wi Si ( vij ) i

The above weighted scoring function implicitly assumes preferential independence of all attributes (Olson 1996). In other words, the preference for any value of an attribute is independent of any value of any other attribute. However, in many real world applications, interactions exist between attribute values. Such preferential dependencies require nonlinear scoring functions, which are seldom used in practice. For a more comprehensive study on the design of multi-attribute auctions see (Bichler, 2001). IBM Research’s ABSolute decision engine (Lee et al., 2001) provides buyers, in addition to standard scoring mechanisms, an interactive visual analysis capability that enables buyers to view, explore, search, compare, and classify submitted bids. An iterative auction mechanism to support multi-attribute procurement was proposed in (Beil & Wein, 2003). The buyer uses an additive scoring function for non-price attributes and announces a scoring rule at the beginning of each round. Through inverse optimization techniques, the buyer learns his optimal scoring rule from the bids of the suppliers. The mechanism is designed to procure a single indivisible item. An English auction protocol for multi-attribute items was proposed in (David et al., 2002), which again uses weighted additive scoring function to rank the bids. All the above mechanisms solve the incomparability between the bids, due to multiple attributes, by assigning a single numerical value to each bid and then ranking the bids by these values. Multi-criteria auction proposed in (Smet, 2003) is an iterative auction which allows incomparability between bids and the sellers increment their bid value by bidding more in at least one attribute. Iterative multi-attribute auctions for procurement were proposed in (Parkes & Kalagnanam, 2005) for procuring a single item. The bid consists of a price for each attribute and the iterative format provides feedback to the suppliers to update their bid prices. 5.2 Multi-criteria optimization for bid evaluation In multiple criteria decision making situations with large or infinite number of decision alternatives, where the practical possibility of obtaining a reliable representation of decision maker’s utility function is very limited, multiple criteria optimization techniques are useful approaches. Multiple attributes can be used both in bid definition and bid evaluation (winner determination). In the following, we describe the use of multiple criteria in bid evaluation using goal programming (adapted from Kameshwaran et al. (2007)). In (Beil &

88


Weun, 2003), the attributes are distinguished as endogenous (bidder controllable) and exogenous from the bidders’ perspective. Attributes in bid definition (or RFQ) provide a means to specify a complex product or service, whereas in bid evaluation, the buyer can use multiple attributes to select the winning bidders. Therefore in bid definition, all attributes should be endogenous for the bidders, whereas in bid evaluation, the buyer can use some exogenous attributes to select the winners. In the MCDM literature, the words criteria and attribute are used interchangeably, and are defined as descriptors of objective reality which represent values of the decision makers (Zeleny, 1982). We associate the word attribute with the RFQ and bids i.e. the buyer declares in the RFQ various attributes of the goods. We use the word criteria to indicate the objectives defined by the buyer for evaluating the bids. For example, if the attributes defined in the RFQ are cost, delivery lead time, and delivery probability, and then the criteria used by the buyer for evaluating the bids can be total cost, delivery lead time, and supplier credibility. With the above norm established, a criterion for evaluating the bids may consist of zero, one, or many attributes defined in the RFQ. For example, the criterion that the winning supplier should have high credibility, is not an attribute defined in the RFQ but private information known to the buyer. On the other hand, minimizing cost of procurement is a function of many attributes defined in the RFQ. Thus criterion is used here in the sense of an objective. Multiple criteria optimization problems can be solved using various techniques like goal programming, vector maximization, and compromise programming (Steuer, 1986; Romero, 1991). We describe here the use of (goal programming) GP to solve the bid evaluation problem. Unlike many multiple criteria optimization techniques which require special software tools, GP can be handled by commercial linear and nonlinear optimization software packages with minimal modifications. In GP, the criteria are given as goals and the technique attempts to simultaneously achieve all the goals as closely as possible. For example, the cost minimization criterion can be converted to the goal: Cost ≤ $20, 000, where $20, 000 is the target or aspiration level. When the target levels are set for all criteria, GP finds a solution that simultaneously satisfies all the goals as closely as possible: It is more of a satisficing technique than an optimizing technique. The goal g can be any of the following types: greater than or equal to (≥ tg) less than or equal to (≤ tg) equality (=tg) range ( ∈ [t 'g , t 'g' ] ) The tg’s are the target or aspiration levels. Without loss of generality let us assume the following goal structure for the procurement problem: goal {c 1 X = f 1 }

( f 1 ≥ t1 )

goal {c 2 X = f 2 }

( f 2 ≤ t2 )

goal {c 3 X = f 3 }

( f 3 = t3 )

(29)

# goal {cG X = f G }

subject to X ∈F

( f G ∈ [t G' , t G'' ]) (30)

89


The X is the vector of decision variables belonging to the feasible set F . The constraint set X ∈ F can be explicitly defined by linear inequalities. For brevity, we will use the above implicit representation. To convert the above GP to a single objective mathematical program, a deviational variable is defined for each goal. It essentially measures the deviation of the respective goal from its target value. Following goal constraints are added to the constraint set (30):

c 1 X + γ 1+ ≥ t 1 c 2 X − γ 2− ≤ t 2 c 3 X + γ 3+ − γ 3− = t 3 (31)

#

cG X + γ G+

≥ t G'

cG X − γ G− ≤ t G'' all γ ≥ 0 The range goal gives rise to two constraints but the other goals lead to only one each. The γ g+ measures the deviation away from the goal in the positive direction and γ g− is for the negative direction. The above goal constraints do not restrict the original feasible region F. In effect, they augment the feasible region by casting F into a higher dimensional space (Steuer, 1986). The GP techniques vary by the way the deviational variables are used to find the final solution. We present here the weighted GP technique for solving the bid evaluation problem. Weighted GP (WGP) or Archimedian GP uses weights, given by the buyer, to penalize the undesirable deviational variables. The buyer specifies the weight κ g+ /− for goal g. The weights measure the relative importance of satisfying the goals. The GP (29) will then be the following single objective programming problem: min ∑ κ g+ /−γ g+ /−

(32)

(31) and X ∈ F

(33)

g

subject to

The goals are generally incommensurable (for example, cost minimization is measured in currency whereas minimizing lead time is measured in days) and the above objective function is meaningless as the weighted summation includes different units. The most intuitive and simplest way would be to express g as percentage rather than as absolute value (Romero, 1991). For e-sourcing, the buyer can specify maximum deviation allowed for a goal and then use the percentage of deviation in the objective function. The multi-attribute sourcing techniques described in this section are extremely useful for sourcing complex goods and services, but they are not wide spread in practice as one would expect. The main hurdle is the lack of exposition of the purchase managers and vendors to these techniques. It is only a matter of time till they are convinced of the profitability of these techniques at the cost of the high complexity, like in the case of combinatorial and volume discount auctions.

90


5.3 Configurable bids Configurable bids are used for trading complex configurable products and services like computer systems, automobiles, insurances, transportation, and construction (Bichler et al., 2002). Configurable bids are an extension of multi-attribute bids. A multi-attribute bid is a set of attribute-value pairs, where each pair denotes the value specified by the bidder for the corresponding attribute. In a configurable bid, the bidder can specify multiple values for an attribute. The buyer can configure the bid optimally by choosing an appropriate value for each of the attributes. Indices i Attribute identification k Value identification j Supplier identification Configurable bid from j {c ijk } i ,k

where c ijk is the cost of value k for attribute i

Decision variables x ijk = 1 if value k is chosen for attribute i for supplier j The above bid structure implicitly assumes that the total cost is the sum of the individual costs incurred for each attribute. This may not be realistic but on the other hand, defining a cost function over a space of attribute-value pair is pragmatically impossible for the buyer. For example, a bid for 10 attributes with 5 values for each should consider a space of 9.7 million possible configurations. The additive cost structure generally works fine, except for certain constraints. For example, while configuring a computer system, a particular operating system may require a minimum amount of memory but not vice versa. Such logical constraints are not uncommon. Also, such logical constraints can be used to model non-additive cost structures like discounts and extra costs. The logical constraints can be converted into linear inequalities (probably with additional binary variables) and hence can be added to the winner determination problem. Buyer’s constraints like homogeneity of values for a particular attribute in multi-sourcing can also be added as constraints to the optimization problem. The configurable bids and in general, multi-attribute sourcing is not widely used in practice despite the theoretical popularity. Even the laboratory experiments showed encouraging results. Multi-attribute auctions with three different settings were experimented in laboratories: (1) with buyer’s scoring function fully revealed for two attributes (Bichler, 2000), (2) with buyer’s scoring function not revealed for three attributes (Strecker, 2003), and (3) with partial revelation of the scoring function for three attributes (Chen-Ritzo et al., 2005). All the three showed that multi-attribute auction formats outperform single attribute auctions. Though rarely used in practice currently, one can expect to see its wide spread usage in near future.

6. Global sourcing Advent of global markets enhanced the emergence of global firms which have factories in different countries. Manufacturers typically set up foreign factories to benefit from tariff and trade concessions, low cost direct labor, capital subsidies, and reduced logistics costs in foreign markets (Ferdows, 1997). Global sourcing is used as a competitive strategy by firms to face the international competition, where suppliers located worldwide are selected to


91

meet the demands of the factories, which are also located internationally (Gutierrez & Kouvelis, 1995; Velarde & Laguna, 2004). The main reasons are lower costs, improved quality, operational flexibility, and access to new technology. Global sourcing is also used synonymously with outsourcing by some authors. In this chapter, global sourcing is used to denote international sourcing or international purchasing. In particular, we define global sourcing as procuring from a set of suppliers located worldwide to meet the demands of a set of factories, which are also located worldwide. Thus, there is no single buyer, but a set of buyers (factories belonging to the same company). Consider a company with many factories located domestically in a region. The purchasing department usually aggregates the demands of all the factories (to gain volume discount) and conducts e-sourcing auction for procurement. There is no distinction between the different factories from the suppliers’ perspective, as usually they belong to the same region. Consider a multinational company with a set of factories located worldwide. The classical way of managing a multinational is to operate each firm as a domestic firm in its respective country. In the last two decades, global firms started adopting integrated management strategies, which blurs the national borders and treat the set of factories from different countries as a part of the same supply chain network. Global sourcing is one such integrated strategy, where suppliers located worldwide are selected to meet the demands of the factories, which are also located internationally. In this section, we present the design of global sourcing network, which is the equivalent to the winner determination problem in the global sourcing scenario. Global sourcing network (GSN) is a set of suppliers in various countries to support the demands of the firm’s international factory network. There are two kinds of decisions that are made in the design of GSN: Supplier selection: The subset of suppliers to be included in the sourcing network. This is a strategic investment decision that is made at the beginning of planning horizon, which incurs the one-time supplier development costs to the firm. Order allocation: The allocation of orders from the selected suppliers to the factories to meet the demand at the factories. This is a tactical decision, influenced by the procurement costs. The first decision is implemented before the planning horizon and the second is implemented during it. This is a single-period problem as there is only one order allocation. The supplier selection decision is assumed fixed and irreversible during the planning horizon i.e. no new suppliers can be added once the decision is made. Each supplier has a fixed development cost, which is the cost of including the supplier in the network. The objective is to minimize the total procurement cost that includes both the supplier development costs and the order allocation costs. Hence, both the decisions are contingent on each other and are made in tandem. In addition to the suppliers, we consider two other sources of supply: Redundant inventory and spot purchase. Redundant inventory is a part of strategic decision, which once invested incurs a fixed cost irrespective of whether it is used or not. Thus it has a fixed cost and a maximum capacity associated with it. Spot purchase is another option that has no strategic component. If all other sources are unavailable, the organization can always go for this sure but costlier option. We assume that the capacity is infinite. The cost incurred due to lost in sales or unmet demand can also be modelled using this option. It essentially has the same characteristics: No fixed cost; no upper limit; sure but costlier option. All the above can be summarized as follows.

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Parameters International factory network: The number of factories and their locations are assumed to be known and fixed. Index i is used as the factory identifier. Potential suppliers: The potential global suppliers are assumed to be known and their locations are fixed. Suppliers are identified by index j. Demand: The demand for the item to be sourced at factory i is di. Supply: The available supply quantity from supplier j is given as range [aj, zj], which denotes the minimum and maximum quantity that can be procured from the supplier. Supplier development costs: The fixed cost of developing supplier j is Fcj if he is accepted in the sourcing network. Procurement costs: Unit cost of procurement from supplier j for factory i is cij. Redundant inventory: A possible investment in redundant inventory for each factory i with capacity ri and total cost Ici. It is more realistic to assume different levels of investments with varying capacity and cost ril , Ic il . For the sake of brevity, we assume only one level of investment for each factory. The proposed model can be easily extended to include various levels. Spot purchase: For each factory i, there is a sure source of supply with unit cost Sci and infinite capacity. Penalty incurred due to lost sales of unmet demand can also be modelled similarly. We have just restricted to one option of this kind per factory for the sake of brevity. The design of GSN involves identifying an optimal set of suppliers, order allocation from the winning suppliers, investments in the redundant inventories, and the quantity to be spot purchased for the factory network, such that the total cost of procurement is minimized. Decision variables xi = 1 if supplier j is included in the network, = 0 otherwise yij Quantity supplied from supplier j to factory i ui = 1 if investment is made for redundant inventory at factory i wi Spot purchase quantity at factory i MILP formulation

{(

)}

min ∑ Fc j x j + ∑∑ c ij y ij + ∑ Ic i ui + ∑ Sc i wi j

i

j

i

(34)

i

subject to

∑ y ij + wi + ri ui ≥ di

∀i

(35)

j

a j x j ≤ ∑ y ij ≤ z j x j

∀j

(36)

i

x j ∈ {0 ,1} y ij ≥ 0

∀j

(37)

∀i, j

(38)

ui ∈ {0 ,1}, wi ≥ 0 ∀i (39) The above problem is the same as the capacitated version of the well studied facility location problem (Drezner & Hamacher, 2002) with the suppliers as the facilities and the factories as


93

the markets with demands. The developing cost of a supplier is the fixed cost associated with opening of a new facility. Many of the algorithms for facility location problem can be adapted for solving the design of GSN problem.

7. Robust e-sourcing Current supply chains are characterized by leanness and JIT principles for maximum efficiency, along with a global reach. This makes the supply chain highly vulnerable to exogenous random events that create deviations, disruptions, and disasters. A strike at two GM parts plants in 1998 led to the shutdowns of 26 assembly plants, which ultimately resulted in a production loss of over 500,000 vehicles and an $809 million quarterly loss for the company. An eight-minute fire at a Philips semiconductor plant in 2001 brought its customer Ericsson to a virtual standstill. Hurricanes Katrina and Rita in 2005 on the U.S. Gulf Coast forced the rerouting of bananas and other fresh produce. In December 2001, UPF-Thompson, the sole supplier of chassis frames for Land Rover’s Discovery vehicles became bankrupt and suddenly stooped supplying the product. Much writings in the recent past as white papers, thought leadership papers, and case studies on supply chain risk management have emphasized that redundancy and flexibility are pre-emptive strategies that can mitigate loses under random events. But this is against the leanness principles and increases the cost. It is required to trade-off between the leanness under normal environment and robustness under uncertain environments. It is in this context; this section briefly introduces robustness, a characteristic of winner determination that is almost neglected in current e-sourcing. Caplice & Sheffi (2006), who were directly involved in managing more than hundred sourcing auctions for procuring transportation services, emphasize on the significance of robustness in bid evaluation. The supplier bankruptcy, transportation link failure, change in demand are common sources of uncertainties that are need to be taken into account during bid evaluation. 7.1 Deviations and disruptions The uncertainties in supply chains might manifest in the form of deviations, disruptions, or disasters (Gaonkar & Viswanadham, 2004). The deviations refer to the change in the certain parameters of the sourcing network like the demand, supply, procurement cost, and transportation cost. The deviations may occur due to macroeconomic factors and the default sourcing strategies may become inefficient and expensive under deviations. Disruptions change the structure of the supply network due to the non-availability of certain production, warehousing and distribution facilities or transportation options due to unexpected events caused by human or natural factors. For example, Taiwan earthquake resulted in disruption of IC chip production and the foot-and-mouth disease in England disrupted the meat supply. Under such structural changes, the normal functioning of supply chain will be momentarily disrupted and can result in huge losses. The third kind of risk is a disaster, which is a temporary irrecoverable shut-down of the supply chain network due to unforeseen catastrophic system-wide disruptions. The entire US economy was temporarily shutdown due to the downturn in consumer spending, closure of international borders and shut-down of production facilities in the aftermath of the 9/11 terrorist attacks. In general, it is possible to design supply chains that are robust enough to profitably continue operations

94


in the face of expected deviations and disruptions. However, it is impossible to design a supply chain network that is robust enough to react to disasters. This arises from the constraints of any system design, which is limited by its operational specification. First, we characterize the deviations and disruptions that can happen in a sourcing network. The three parameters that influence the sourcing decision are: Demand, supply, and procurement cost. The demand is the buyer’s parameter, whereas the supply and the cost are given in the bids by the suppliers. In terms of bid evaluation as a mathematical program, the objective coefficients are the costs and the demand-supply parameters are the right hand side constants of the constraints. The optimal solution to the above mathematical program obviously depends on the three parameters. However, all the three are subject to deviations: cost deviation due to macroeconomic change or exchange rate fluctuations supply disruption due to supplier bankruptcy transportation link failure due to natural calamity or port strike, leading to supply disruption supply deviation due to upstream supply default demand deviation due to market fluctuation The above deviations and disruptions are realized after the bid evaluation but before the physical procurement. Thus, these deviations can render the optimal solution provided by the bid evaluation costly and inefficient, and even sometimes infeasible and inoperable. To handle unforeseen events in sourcing network or in general, supply chain network, there are two obvious approaches: (1) to design networks with built in risk-tolerance and (2) to contain the damage once the undesirable event has occurred. Both of these approaches require a clear understanding of undesirable events that may take place in the network and also the associated consequences and impacts from these events. We show here how we can design a risk-tolerant sourcing network by taking into account the uncertainties in bid evaluation. 7.2 Bid evaluation under uncertainty Bid evaluation problem is an optimization problem and hence we can draw upon the optimization techniques that can handle randomness in data. The decision-making environments can be divided into three categories (Rosenhead et al., 1972): certainty, risk, and uncertainty. In certainty situations, all parameters are deterministic and known, whereas risk and uncertainty situations involve randomness. In risk environments, there are random parameters whose values are governed by probability distributions that are known to the decision maker. In uncertainty environments, there are random parameters but their probability distributions are unknown to the decision maker. The random parameters can be either continuous or discrete scenarios. Optimization problems for risk environments are usually handled using stochastic optimization and that for uncertain environments are solved using robust optimization. The goal of both the stochastic optimization and robust optimization is to find a solution that has acceptable level of performance under any possible realization of the random parameters. The acceptable level is dependent on the application and the performance measure, which is part of the modelling process. Stochastic optimization problems (Birge & Louveaux, 1997; Kall & Wallace, 1994) generally optimize the expectation of the objective function like minimizing cost or maximizing profit. As probability distributions are known and expectation is used as the performance measure,


95

the solution provided is ex-ante and the decision maker is risk neutral. Robust optimization (Kouvelis & Yu, 1994) is used for environments in which the probability information about the random events is unknown. The performance measure is hence not expectation and various robustness measures have been proposed. The two commonly used measures are minimax cost and minimax regret. The minimax cost solution is the solution that minimizes the maximum cost across all scenarios, where a scenario is a particular realization of the random parameters. The minimax regret solution minimizes the maximum regret across all scenarios. The regret of a solution is the difference (absolute or percentage) between the cost of that solution in a given scenario and the cost of the optimal solution for that scenario. Both the approaches have been used to solve the sourcing problem with randomness. A robust optimization based approach for uncapacitated version of the sourcing problem with exchange rate uncertainty (cost deviation) was considered in (Gutierrez & Kouvelis, 1995; Kouvelis & Yu, 1997). The uncertainties were modelled using discrete scenarios and minimax regret criterion was used to determine the robust solution. In (Velarde & Laguna, 2004), the deviations in both demand and exchange rates were considered. The randomness was modelled using discrete scenarios with probabilities. The objective function had two components: expected cost and variability (that measures the risk). In the following we outline a robust optimization based approach to solve the bid evaluation problem. 7.3 Robustness approach to bid evaluation The objective here is to propose a robust optimization methodology to design a sourcing network that is risk-tolerant. The choice of robust optimization is due to the fact that managers are more concerned about the outcome of a random event than its probability of occurrence (March & Shapira, 1987). Hence, the optimization of expected cost approach, which implicitly assumes the decision maker to be risk neutral, is not directly applicable. It was also noted by Gutierrez & Kouvelis (1995) that decisions of the managers are not evaluated by their long term expected outcome but by their annual or half-yearly performance. Hence, robust optimization that directly works with the outcome of the random events, rather than probability and long-run expected outcomes, is more appropriate for e-sourcing. In the proposed methodology, the randomness is modelled via discrete scenarios. The advantage with discrete scenarios is that one need not concern about the source of the scenario, but rather work with the scenario directly. For example, a supplier might get disrupted due to several reasons: Bankruptcy, transportation link failure, upstream supply failure, etc. The buyer needs to only concern about the scenario of a particular supplier failing rather than the sources that would cause it. Working at the level of scenarios is complicated for probability models, as one has to derive the probability of a scenario from the probabilities of the random events that are responsible for that scenario. With no probability information required for robust information, discrete scenario modelling is more appropriate for sourcing. In the following, we abstract the bid evaluation problem to be an optimization problem without specifying the bid structure and the business constraints. Indices s Scenario identifier Data Ds Demand vector in scenario s A s Supply vector in scenario s

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Cs Cost vector in scenario s Notation X A solution vector to the bid evaluation problem Xs Optimal solution vector to bid evaluation in scenario s XR Robust solution vector s Z (⋅) Cost of solution (⋅)

A scenario s is characterized by a 3-tuple of vectors: {Ds , A s , C s } . Thus, any change in demand or supply or cost or their combinations represents a scenario. By definition, any two scenarios will differ in at least one of the D, A, C vectors. Let s=0 denote the default or unperturbed scenario. The lowest cost Ls for scenario s is its optimal cost: Ls = Z s (X s )

(40)

From the optimality of X s , it follows that for any solution X, Ls ≤ Z s (X ) . The relative regret of solution X for scenario s is: r s (X ) =

Z s (X) - Ls Ls

(41)

Let U s denote the maximum cost that will be incurred for scenario s. If U s − Ls is negligible, then the scenario is not sensitive to the solution. On the other hand if U s >> Ls , the scenario s has to be judiciously handled, even if it is a low probable event, as it might end up with huge increase in the cost. The objective function is robust optimization is a robustness measure. The two commonly used measures are minimax cost and minimax regret. The minimax cost solution is the solution that minimizes the maximum cost across all scenarios, where a scenario is a particular realization of the random parameters. The minimax regret solution minimizes the maximum regret across all scenarios. The minimax regret objective is given by: min max r s ( X ) X

(42)

s

In general, the minimax versions are overly conservative as the emphasis is on the worst possible scenario, which may occur very rarely in practice. Hence, a solution that is good with respect to the worst-case scenario may perform poorly on the other commonly realizable scenarios. Another measure of robustness is to constrain the regret within prespecified value p s : r s ( X ) ≤ p s (Snyder & Daskin, 2006). Small values of ps make the solution X to perform close to that of the optimal solution Xs for scenario s. Thus, by judiciously selecting ps, the buyer can characterize the importance of scenario s. To implement the above, the following constraints are included in the formulation for robust design. Z s ( X ) ≤ (1 + p s )Ls s

∀s

(43)

Note that L is the optimal cost for scenario s, and hence for robust design, one needs to solve the bid evaluation problem for each of the scenarios. For any solution X, Ls ≤ Z s ( X ) ≤ U s . Combining with constraint (43), one can derive the maximum value for p s :

97


ps ≤

Us −1 Ls

∀s

(44)

The { p s } are the input parameters to be provided by the buyer to define the acceptable levels of operation for different scenarios. Determining U s will aide the buyer in choosing an appropriate value for p s . Let X R be a robust solution that satisfies the constraints (xx). Then, r s (X R ) ≤ ps

∀s

(45)

Given a set of robust solutions { X R } , the buyer can choose the best one based on different business criteria. The robust solution ensures that the sourcing network operates at the predetermined operating levels under a wide range of pre-identified scenarios. Thus, the usefulness of the approach clearly depends on the number and the nature of the representative scenarios identified. However, constraint set (43) requires that the winner determination problem needs to be solved for every scenario. As noted in the previous sections, winner determination problems are computationally challenging for complex bid structures and in the presence of business constraints. Thus, the need for solving it for each of the scenarios limits the number of scenarios that can be considered in the robust design.

8. Final notes This chapter was devoted to optimization based e-sourcing models. It reviewed three popular e-sourcing techniques with their underlying mathematical programming models that are used to solve the winner determination problems. The volume discount and combinatorial sourcing are actively used in business-to-business commerce saving billions of dollars annually. The multi-attribute sourcing technique is yet to catch up, but one can expect increased use in near future given its popularity in literature and encouraging results in laboratory experiments. All the three techniques reviewed in the chapter are provided commercially as e-sourcing tools by many vendors. We also presented in this chapter two future directions, which are inevitable in the evolution of e-sourcing techniques and tools. One is global sourcing, which connects multiple suppliers with multiple factories, all located internationally. The second is the robust sourcing that takes into account deviations and disruptions, which can render the solution provide by traditional e-sourcing tools inefficient and costly. We presented both the above in the framework of optimization based e-sourcing and hence the currently available methodologies and tools can be adapted to include them.

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6 A Domain Engineering Process for RFID Systems Development in Supply Chain Leonardo Barreto Campos1, Eduardo Santana de Almeida2, Sérgio Donizetti Zorzo3 and Silvio Romero de Lemos Meira4 1Federal

2Recife

University of Vale do São Francisco (UNIVASF) Center for Advanced Studies and Systems (CESAR) 3Federal University of São Carlos (UFSCar) 4Federal University of Pernambuco (UFPE) Brazil

1. Introduction According to the Supply-Chain Council (1997), the supply chain encompasses every effort involved in producing and delivering a final product or service, from the supplier's supplier to the customer's customer. Supply Chain Management (SCM) includes managing supply and demand, sourcing raw materials and parts, manufacturing and assembly, warehousing and inventory tracking, order entry and order management, distribution across all channels, and delivery to the customer. In this context of several sources of information exchanging data dynamically in supply chain, the Radio Frequency Identification (RFID) appears as a technology able to identify objects such as manufactured goods, animals, and people. Thus, the goal of the RFID technology in supply chain management is to guarantee interoperability providing, for example, accurate and real-time information on inventory of the organizations, product recalls and communications among supply chain participants. On the other hand, the RFID-based systems used in supply chain management were not considered by a specific software development process. In this scenario, a process is important and necessary to define how an organization performs its activities, and how people work and interact in order to achieve their goals. In particular, processes must be defined in order to ensure efficiency, reproducibility and homogeneity (Almeida, 2007). There are several definitions on software process (Osterweil, 1987), (Pressman, 2005), and (Sommerville, 2006). According to Ezran et al. (2002) software processes refer to all the tasks necessary to produce and manage software, whereas reuse processes are the subset of tasks necessary to develop and reuse assets (Ezran et al., 2002). The adoption of either a new, well-defined, managed software process or a customized one is a possible facilitator for success in reuse programs (Morisio et al., 2002). In supply chain domain, many scenarios and processes are repeatable among supply chain participants (sub-domains), for example, inventory management, shipment and delivery of the goods, and localization of a product. In this sense, software reuse – the process of creating software systems from existing software rather than building them from scratch – is a key aspect for improving quality and productivity in the software development.

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In the context of software reuse, important research including company reports (Bauer, 1993), (Endres, 1993), (Griss, 1994), (Joos, 1994), (Griss, 1995), informal research (Frakes & Isoda, 1995), (Frakes & Kang, 2005) and empirical studies (Rine, 1997), (Morisio et al., 2002), (Rothenberger et al., 2003) have highlighted the relevance of a reuse process, since the most common way of software reuse involves developing applications reusing pre-defined assets. The software reuse processes literature focuses on two directions: Domain Engineering and, currently, Software Product Lines (in section 3 a more detailed discussion about it is presented). Thus, motivated by increasing utilization of the RFID technology in supply chain and the lack of specific development processes for the RFID-based systems development in supply chain, this chapter aims at defining a systematic process to perform domain engineering which includes the steps of domain analysis, domain design, and domain implementation. In the next section we present the parts of the EPCglobal Network. Eight software reuse processes distributed in domain engineering and software product lines are discussed in the followed section. This is followed by an overview of the proposed domain engineering process. The Sections 5, 6 and 7 describe the domain analysis, domain design, and domain implementation steps respectively. Finally, the conclusion summarizes the contributions this work and directions for future works.

2. The EPCglobal network One critical issue of the new technologies is their standardization. In case of the RFID systems, both EPCglobal and International Standards Organization (ISO) have adopted RFID in their standards. According (Sabbaghi & Valdyanathan, 2008) the most prominent industry standards for RFID are the EPCglobal specifications and standards for supply chain. The EPCglobal Inc is a nonprofit organization that was initiated in 2003 by MIT AutoID Center in cooperation with other research universities to establish and support the EPC Network as the global standard for the automatic and accurate identification of any item in supply chain. The EPCglobal is establishing the standards on how information is passed from RFID readers to various applications, as well as from application to application, in the supply chain. These standards are specified in EPCglobal Architecture Framework, or simply EPCglobal Network. Its is a collection of interrelated hardware, software, and data standards, together with core services that are operated by EPCglobal and is delegates, all in service of a common goal of enhancing business flows and computer applications through the use of Electronic Product Codes (Armenio, 2007). It is composed of five components: (i) Electronic Product Code, (ii) Identification System, (iii) EPC Middleware, (iv) Discovery Services, and (v) EPC Information Service. Firstly, the Electronic Product Code (EPC) is defined as “a naming and identification scheme designed to enable the unique identification of all physical and virtual objects, assemblies and grouping of objects, and non-objects such as service” (Engels, 2003). It is incorporated into a RFID chip and attached to a physical object. An Electronic Product Code is comprised of header and more three distinct numbers: domain manager number, object class number, and serial number. In this way is possible to provide information about product or object such as your category, data and time of manufacture, final destination, etc. The Identification System consists of RFID tags and RFID readers. RFID Tag is an electronic

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device composed of microchip and an antenna attached to a substrate, as shown in Figure 1. On the other hand, the RFID readers create a radio frequency field that detects radio waves. When a tag passes through a radio frequency field generated by a compatible reader, the tag reflects back to the reader the identifying information about the object to which it is attached, thus identifying that object.

Fig. 1. Electronic Product Code and RFID Tag Next, the EPC Middleware manages real-time read events and information, provides alerts, and manages the basic read information for communication to EPC Information Services and a company’s other existing information systems. The Discovery Services returm locations that have some data related to an EPC (EPCglobal, 2005). In general, a Discovery Services may contain pointers to entities other than the entity that originally assigned the EPC code. Hence, Discovery Services are not universally authoritative for any data they may have about an EPC. The important service in Discovery Services is the Object Name Service (ONS) that, given an EPC, can return a list of network accessible service endpoints that pertain to the EPC in question. Finally, the EPC Information Service (EPCIS) provides an uniform programmatic interface to allow various clients to capture, secure, and access EPCrelated data and the business transactions which that data is associated (Harrison, 2003). Companies that assign EPC numbers can maintain EPC Information Service servers with item information. Using EPC numbers does not require organizations to share EPC data or use other components of the system. The EPCglobal Network presented previously contains several aspects that can be considered by Software Reuse. According to (Harrison, 2003), the hardware, software, and Interfaces defined in EPCglobal Network are management by applications with networked databases. In this sense, software reuse can be used in development of applications, reusing assets available in domains of the Supply Chain. For example, the localization of a product in supply chain is divided into six steps: (i) the RFID reader capture the EPC stored on the tag, (ii) EPC Middleware verify and validate the EPC, (iii) EPC Information Service search data related to EPC in local ONS and return the result, (iv) next, the supply chain participant authenticate it in the EPCglobal Network, (v) ONS search data related to EPC in external databases using EPCglobal Network infrastructure, and (vi) return the search results to application as shown in Figure 2. This scenario and others situation are commons for some supply chain domains. Therefore, the goal of domain engineering process described in this chapter is to identify common and specific features, scenarios, domain-specific software architecture, and so on, for analysts and designers in a domain, as well as simplifying the identification and implementation of the software components. The next section presents an analysis involving eleven software reuse processes discussing their, fundamentals, concepts, pros and cons that consists a base for the process defined in this chapter.

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Fig. 2. Scenario of utilization of the EPCglobal Network

3. Software reuse processes Since the time that software development started to be discussed within the industry, researchers and practitioners have been searching for methods, techniques, and tools that would allow for improvements in costs, time-to-market and quality (Almeida, 2007). The software reuse processes encompass concepts, strategies, techniques, and principles that enable developers to create new systems effectively using previously developed architectures and components. A software reuse process, besides presenting issues related to non-technical aspects (education, culture, organizational aspects, etc), must describe two essential activities: the development for reuse (Domain Engineering), which will be discussed next and the development with reuse (Application Engineering) which consists in building applications based on the assets produced in Domain Engineering. In the state-of-art of the software reuse processes presented in (Almeida et al., 2005) and the discussions about it in (Frakes & Kang, 2005) is possible to note that several research studies aimed at finding efficient ways to develop reusable software. These work focus on two directions: Domain Engineering and, currently, Software Product Lines, as can be seen in the next sections. 3.1 Domain engineering Domain Engineering is defined in (Czarnecki & Eisenecker, 2000) as “the activity of collecting, organizing, and storing past experience in building systems or parts of systems in a particular domain in the form of reusable assets, as well as providing an adequate means for reusing these assets


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when building new systems”. In this context, this work analysed four domain engineering processes. The first domain engineering approach is called Draco. This approach is based on transformation technology and was developed by James Neighbors in his Ph.D. Work (Neighbors, 1989). The main idea introduced by Draco is to organize software construction knowledge into a number of related domains. Each Draco domain encapsulates the needs and requirements and different implementations of a collection of similar systems. In this work, is presented also, an initial direction to development software using Domain Engineering. However, his approach is very difficult to apply in the industrial environment due the complexity to perform activities such as writing transformations and using the Draco machine. Described in the 90’s, Feature-Oriented Domain Analysis (FODA) is a domain analysis method developed at the Software Engineering Institute (SEI). The method presented in (Kang et al., 1998) consists of two phases: Context Analysis and Domain Modelling. The major contribution of FODA method is the feature model. An important part of this model is the feature diagram that defines three types of features: mandatory, alternative, and optional features. Next, the Feature-Oriented Reuse Method (FORM) (Kang et al., 2002), seen as an elaboration of the FODA, to present four layers to classify features: capability, operating environment, domain technology, and implementations technique. Moreover, the processes do not include essential domain analysis techniques such as domain scoping. Other important domain engineering method analysed was the Organization Domain Modeling (ODM) developed by Mark Simos. The ODM process described in (Simos et al., 1996) consists of three main phases: Plan Domain, Model Domain, and Engineer Asset Base. However, the phases do not present specific details on how to perform many of its activities. According to (Czarnecki & Eisenecker, 2000) the ODM provides a “general high-level guidance in tailoring the method for application within a particular project or organization”. 3.2 Software product lines A new trend started to be explored in software reuse process is the Software Product Line area. According to (Clements & Northrop, 2001), a software product line is “a set of softwareintensive systems sharing a common, managed set of features that satisfy the specific needs of a particular market segment or mission and that are developed from a common set of core assets in a prescribed way”. In this context, we analysed four software product lines processes. Firstly, we present the Family-Oriented Abstraction, Specification and Translation (FAST) process, described in (Weiss & Lai, 1999). The FAST process focuses on pattern for software production processes that strives to resolve the tension between rapid production and careful engineering. The FAST process consists of three well-defined sub-processes: domain qualification, domain engineering, and application engineering. On the other hand, some activities in the process such as in Domain Engineering are not as simple to perform, for example, the specification of an Application Modeling Language. Presented in (Atkinson, 2000), the Komponentenbasierte Anwendungsentwicklung (KobrA) approach provides a generic assets that can accommodate variants of a product line through framework engineering. The gap in KobrA approach is does not present guidelines to perform systematic tasks such as domain analysis and domain design. An effort to apply the reuse concepts within the embedded systems domain is described in (Winter et al., 2002). The Pervasive Component Systems (PECOS) approach focuses on two issues: how to enable the development of families of PECOS devices? And how pre-fabricated components have

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to be coupled? Some gaps was identified in PECOS, for example, in component development, there is not guidance on how the requirements elicitation is performed, and how the components are identified. Finally, is presented in (Gomaa, 2005) the Product Line UML-Based Software Engineering (PLUS) approach considered the most current process related to product lines. PLUS extends UML by integrating several product line engineering techniques to support UMLbased product line engineering. PLUS defines three general steps: requirements, analysis, and design. Modelling in order to provide various modelling techniques and notation for product line requirements engineering activity, use case modelling, and feature modelling. However, in requirements and analysis, activities related to scoping are not considered.

4. The domain engineering process According to the previous Section, software reuse can be an important way to develop software, offering benefits related to quality, productivity and costs, mainly, using a welldefined reuse process. However, the current software reuse processes present gaps and lack of details in key activities such as, for example, domain engineering. Other requirements like application engineering, metrics, economic aspects, reengineering, adaptation, and quality are important as well, but focus on this chapter is on domain engineering. In this context, this section presents an overview of the proposed domain engineering process, its foundations, and steps. 4.1 Overview of the process As defined in the previous chapter, domain engineering is the activity of collecting, organizing, and storing past experience in building systems or parts of systems in a particular domain in the form of reusable assets (i.e. reusable work products), as well as providing an adequate means for reusing these assets (i.e. retrieval, qualification, dissemination, adaptation, assembly, and so on) when building new systems (Czarnecki & Eisenecker, 2000, pp. 20). A domain engineering process should define three important steps: Domain Analysis (DA), Domain Design (DD), and Domain Implementation (DI). In general, the main goal of Domain Analysis is domain scoping and defining a set of reusable, configurable requirements for the systems in the domain. Next, Domain Design develops a common architecture for the system in the domain and devising a product plan. Finally, Domain Implementation implements the reusable assets, for example, reusable components, domain-specific languages, generators, and a production process (Czarnecki & Eisenecker, 2000). Before presenting more details about the proposed domain engineering process, it is important to discuss two basic concepts which will be used next: (i) Domain: encompasses not only the real world knowledge in a given problem area, but also the knowledge about how to build software systems in that area, corresponding to the domain as a set of systems view, (ii) Feature: is widely used in domain analysis to capture the commonalities and variabilities of systems in a domain. In general, there are two definitions of features found in the reuse literature. The first says that an end-user-visible characteristic of a system, the FODA definition. The second definition about feature says that it is a distinguishable characteristic of a concept (e.g. system, component, etc) that is relevant to some stakeholder of the concept, the ODM definition (Simos et al., 1996). In this work, the first definition will be used, since it is the base for the domain analysis area. Other important concepts, more


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specific to each step of the process, will be presented later, together with the process detailed description. 4.2 The foundations A software development process can be understood as the set of activities needed to transform an user’s requirements into a software system. The way it is done can change from process to process. For example, processes can be focused on domain engineering, services (Papazoglou & Georgakopoulos, 2003), or Model-Driven Development (MDD) (Schmidt, 2006), or use different process models, however, all kind of process is based on some foundations. In this section, the foundations for the domain engineering process will be presented. Process Model. A software process model is an abstract representation of a software process (Sommerville, 2006). Each process model represents a process from a particular perspective, and thus provides only partial information about that process. There are different process models published in the software engineering literature, such as Waterfall model, Evolutionary development, Component-Based Software Engineering (CBSE), Incremental delivery, Spiral development, among others (Pressman, 2005). These process models are widely used in current software development practice. The domain engineering process defined in this thesis is based on the spiral process model (Boehm, 1988), however, it presents some characteristics of the CBSE process model, since reusable assets are used to develop applications. The spiral model proposed originally by Boehm (1988), rather than represent the software process as a sequence of activities with some backtracking from one activity to another, consists of a spiral, where each loop represents a phase of the software development process. The Figure 3 shows an overview of the process according to spiral process model. Domain Driven. Instead of traditional software development processes as, for example, the RUP, which is use-case driven, the domain engineering process is domaindriven, where the focus is on a set of applications for a particular domain. In this domain, the crucial points are: to identify common and specific features from existing, future, and potential applications; to organize this information in a domain model; next, to design the Domain-Specific Software Architecture (DSSA); and, finally, to implement reusable components for that domain. Even being domain driven, use cases are also used in the process as will be shown in section 6. Software Architecture. Software architecture involves the structure and organization by which modern system components and subsystems interact to form systems; and the properties of systems that can be better designed and analysed at system level (Kruchten et al., 2006). Component-Based Development (CBD). Component-based Development techniques are important because in domain design, for example, it is interesting to modularize the architecture in well-defined components, which can be easily changed without affecting other parts of the architecture. Moreover, in domain implementation, whose goal is to develop reusable assets, an important way of doing it is through a set of domain-specific components, increasing the reuse potential. Desig Pattern. A design pattern is a largergrained form of reuse than a class because it involves more than one class and the interconnection among objects from different classes. From the perspective of the domain engineering process, design patterns are important because they can be used to encapsulate the variability existing in domain analysis model and perform the mapping for design.

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Fig. 3. Process model of the domain engineering process. 4.3 Steps of the domain engineering process The process for Domain Engineering defined in this chapter is composed of three steps: Domain Analysis, Domain Design, and Domain Implementation. Due to the amplitude, each step is respectively divided in activities and sub-activities. The steps are defined to be used in sequence. However, even with less optimal results, they can be used separately. Therefore, in this chapter, each step will be treated as an approach for a different part of the domain engineering life cycle. In this sense, there is an approach for Domain Analysis, described in section 5, an approach for Domain Design, described in section 6, and, finally, an approach for Domain Implementation, described in section 7.

5. Domain analysis step The Domain Analysis is considered an important phase in reuse-based software development because it is able to identify common and variable features, analyse the domain scope, define the people (stakeholders) who has a defined interest in the result of the project, etc. The first definition for domain analysis was presented by Neighbors as “the activity of identifying the objects and operations of a class of similar systems in a particular problem domain” (Neighbors, 1989). For Neighbors, the identification of objects and operations commons in domain minimizes effort in systems development. However, Neighbors not present steps of “how to perform” domain analysis. Based in this gap, works as (Arango, 1989), (Prieto-Diaz, 1990) and (Almeida, 2006) focus on the outcome, not on the process. For Prieto-Diaz (1990), domain analysis is: “to find ways to


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extract, organize, represent, manipulate and understand reusable information, to formalize the domain analysis process, and to develop technologies and tools to support it”. In this sense, this section presents a domain analysis approach for engineering RFID-based systems in supply chain. The goal this approach is identify and modelling commons and variables features presents in each domain supply chain (Campos & Zorzo, 2007). The domain analysis consists of four steps: Planning, Requirements, Domain modelling, and Documentation. The next sub-sections presents each step in details. 5.1 Planning Firstly, ours goals whit domain analysis are: (i) description of the domain, (ii) identify the stakeholders, and (iii) domain scoping. Therefore, the planning step is based in three subactivities (P): P1. Domain: encompass to describe which supply chain will be applied the domain analysis (e.g., domain of the healthcare, automotive, food, etc). Next, is necessary to divide the supply chain in domains and describe it into four aspects (A): A1. Activity: that objective of sub-domain in supply chain? A2. Input: from who the sub-domain receives information in supply chain. A3. Output: to who the sub-domain send information in supply chain. A4. Technology: where and which objective to use RFID systems in sub-domain. Hence, the supply chain will be represented how domains that uses RFID systems in specifics activities, inputs, and outputs. P2. Stakeholder analysis: the stakeholders are “people or someone who has a defined interest in the result of the project”. In this sense, many stakeholders can be identified in development and utilization of RFID-based systems in supply chain. For example, the RFID engineering, person that must be expert with EPCglobal Network, RFID readers, RFID tags and yours variabilities, installation, utilization, etc. P3. Domain scope: this step consists in identify and discard domains in supply chain out of scope. Domains that do not send or receive data for other sub-domains are eliminated. Next, the domain scope analysis is made in terms of horizontal scope. This type of analysis has the goal answer the questions: How many different systems are in the domain? Finally, the last step, consists of analysing the domain scope in terms of vertical scope. Such, is important answer the questions: Which parts of these systems are in the domain? In this context, vertical domains contain complete systems. An organization which does not have any experience with Domain Engineering should choose a small but important domain. After succeeding with the first domain, the organization should consider adding more and more domains to cover its product lines. 5.2 Requirements The second step in domain analysis is the requirements elicitation, or simply requirements. The goal is to describe the characteristics of the domain and to understand the users’ needs. The requirements identification process includes stakeholders as manager, engineering, and end user identified in planning activity. The Requirements activity is not an easy task, mainly, because can exist some problems in potential since the domain contains several systems, (Pr) as: Pr1. Ambiguity: stakeholders do not know what they really want. Pr2. Redundancy: requirements of stakeholders different interpreted of the same form. Pr3.Conflicting Requirements: Different stakeholders with conflicting requirements. Pr4. External Factors: domain requirements may be influenced by organizational and political factors. Pr5. Stakeholders Evolution: news stakeholders may emerge during the analysis process. Pr6. Requirements Evolution: change of the requirements during the analysis process.

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Our effort to minimize errors is to make the requirements elicitation through features. The feature definition used in this chapter is in concordance with (Kang et al., 1998): “an enduser-visible characteristic of a system”. After defining the form to extract the domain requirements, is necessary to define as to extract them. In this task, the approach uses the concept of the scenarios. The scenarios are descriptions of how a system is used in practice. Thus, the steps (S) for requirements elicitation from scenarios are: S1. Initial stage: systems stage at the beginning of the scenario. S2: Events: normal flow of events in the scenario. S3. Alternative Events: eventual events out the normal flow that can cause error. S4. Finish stage: systems stage on completion of the scenarios. S5. Stakeholders: to list the stakeholders that had participated in scenarios. 5.3 Domain modelling The Domain Modelling is the third step in domain analysis. Your goal is identifying and modelling commons and variables requirements in domains. In RFID-based systems, the features will be based on the EPCglobal Network and the following sub-activities (M): M1. Commonality analysis: consist in identify which features are commons to all applications of the domain. There are different ways to identify common requirements. This approach uses a based-priority sub-domain-requirements matrix shown in Table 1. The idea is select requirements by priority for all stakeholders.

Req. 1

Dom. 1

Dom. 2

Dom. 3

...

Dom. n

Pr2

Pr1

Pr2

-

-

Req. 2

X

Pr2

Pr3

-

-

Req. 3

Pr1

Pr1

Pr1

-

-

... Req. n

-

-

-

-

-

Table 1. Structure of Based-Priority Domain-Requirements Matrix The left column of the matrix lists the requirements of the considered sub-domains. The subdomains themselves are listed in the top row. In the body of the matrix it is filled by priority of the requirements. The priorities (Pr) are classified as follows: Pr1. High: the requirement ‘Pr1’ is mandatory for all sub-domains and is thus a candidate to be defined as a common domain requirement. Pr2. Medium: the requirements that assists high-priority requirements to keep the functionality of the systems. Pr3. Low: low-priority requirements to systems. After filling of the matrix, the domain analyst must define ideal priority for commons requirements. The second sub-activity is M2. Variability analysis: this activity consists in identifying which features are variable to applications of the domain. According to (Svahnberg et al., 2001) in situations where a lot of effort has been made to preserve variability until very late in the development process, the systems provides greater reusability and flexibility. Finally, we have the sub-activity M3. Domains modelling: here, the commonalities and variabilities are modelled. The model may be applicable at a high level to a number of applications. In this approach the features may be mandatory, optional, or features or alternative as shown Figure 4 (Czarnecki & Eisenecker, 2000): According to Czarnecki and Eisenecker (2000) a mandatory feature node is pointed to by a simple edge ending with a filled circle. An optional feature may be included in the description


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of a concept instance if and only its parent is included in the description. A concept may have one or more sets of direct alternative features. Finally, a concept may have one or more sets of direct or-features. However, if the parent of a set of or-feature is included in the description of a concept instance, then any non-empty subset from the set of or-features is included in the description; otherwise, none are included.

Fig. 4. Features types. Adapted (Czarnecki & Eisenecker, 2000) Documentation. In this activity the requirements, identified in form of features, will be documented. According to (Czarnecki & Eisenecker, 2000) the template used for document features contain the fields:

6. The domain design step The second phase of the domain engineering process defined in this chapter is the Domain Design. The key goal this phase is to produce the domain-specific or reference architecture, defining its main software elements and their interconnections in concordance with (Bosch, 2000). The concept of software architecture as a distinct discipline started to emerge in 1990, and in 1995 (Shaw & Garlan, 1996), the field had a strong grow with contributions from industry and academia, such as methods (Kazman et al., 2005) for software architecture. Our domain design approach use the following concept: “A software architecture is a description of the subsystems and components of a software system and the relations between them. Subsystems and components are typically specified in different views to show the relevant functional and nonfunctional properties of a software system. The software architecture of a system is an artefact. It is the result of the software development activity”, presented by (Clements et al., 2004).

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Feature Name: Semantic Description Each feature should have at least description describing its semantics Rationale A feature should have a note explaining why the feature is included in the model Stakeholders and client programs Each feature should be annotated with stakeholders (e.g., users, customers, developers, managers) who are interested in the feature and the client programs that need this feature Exemplar applications If possible, the documentation should describe features with known applications implementing them Constraints Constraints are had dependencies between variable feature. Two important kinds of constraints are mutual-exclusion constrains and required constrains Open/closed attribute Variation points should be market as open if new direct variable sub-feature (or features) are expected. On the other hand, marking a variation point as closed indicates that no other direct variable sub-feature (or feature) are expected Priorities Priorities may be assigned to features in order to record their relevance to the process The main way of reusing a software architecture is to design a Domain-Specific Software Architecture1 (DSSA) (Tracz, 1995) or Product-Line Architecture2 (Dikel et al., 1997). The difference between software architecture in general and a DSSA is that a DSSA is used by all applications in the domain. In this sense, a DSSA for RFID-based Systems in Supply Chain is defined in the domain design phase and your goal is develop an assemblage of software components, specialized for a particular type of task (domain), generalized for effective use across that domain, composed in a standardized structure effective for building successful applications (Tracz, 2005). The next sections present the activities of the domain design: (i) Mapping, (ii) Components Design, (iii) Architecture Views, (iv) and, Architecture Documentation. 6.1 Mapping The first activity in domain design is the mapping from requirements to reference architecture. An important issue considered in this activity is the variability. According to 1 Term used by the reuse community and adopted in this thesis. 2 Term used by the software product lines community. However, both present the same idea


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(Svahnberg et al., 2001), variability is the ability to change or customize a system. Improving variability in a system implies making it easier to do certain kinds of changes. It is possible to anticipate some types of variability and construct a system in such a way that it facilitates this type of variability. In domains supply chains there are many variabilities, both in use of the RFID technology and in supply chain organization. Therefore, the requirements mapping must keep the variability in order to repeat the process for many different domains and offer a reference for it. Other issue that the domain designer should consider is with components specifications. Some decisions (e.g. algorithms used in component development, objects and types of the component interfaces) can to restrict the component reuse. When these decisions conflict with specific requirements, the components reuse is limited or the system will be inefficient. An efficient way to minimize or eliminate these conflicts is using Design Pattern. According to Christopher Alexander (Alexander et al., 1977) “each pattern describes a problem which occurs over and over again in our environment, and then describes the core of the solution to that problem, in such a way that you can use this solution a million tomes over, without ever doing it the same way twice”. This way, the pattern is a description of the problem and the essence of its solution, so that the solution may be reused in different settings. In the software world, design pattern were popularized by the gang of four (Gamma et al., 1995). According to Gamma and his colleagues, design patterns describe a recurring design problem to be solved, a solution to the problem, and the context in which that solution works. From the perspective of the domain engineering process, design pattern are important because they can be used to encapsulate the variability existing in domain analysis model and perform the mapping for design. In general, a pattern has four essential elements: the pattern name, the problem, the solution, and the consequences. In the approach presented in this chapter, Design Patterns are used, but together with useful guidelines that determine how and when patterns can be used to represent the different kinds of variability that can exist in a DSSA for RFID-based Systems in Supply Chain. In to order to design the variability of each module, we consider that it should be traceable from domain analysis assets (features) to architecture, according to alternative, or and optional features (Lee & Kang, 2004). 6.1.1 Alternative features Alternative features indicate a set of features, from which only one must be present in an application. Thus, the following set of patterns can be used (Gamma et al., 1995): Abstract Factory. The abstract factory pattern provides an interface for creating families of related or dependent objects without specifying their concrete classes. Specifying a class name when the domain designer creates an object commits you to a particular implementation instead of a particular interface. In this way, this pattern can be used to create objects indirectly and assure that only one feature can be present in the application. In RFID-based systems in supply chain, there are several readers and simultaneous readings. Thus, the EPC Middleware must select only one EPC in case of various requisitions and to discard unnecessary information of the data bases as shows Figure 5. Chain of Responsibility. This pattern avoids coupling the sender of a request to its receiver by giving more than one object a chance to handle the request. Objects in a chain of responsibility must have a common type, but usually they do not share a common implementation. In this sense, the same requisitions realized in distinct domains can be

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resolved by different objects. For example, the Discovery Service can want to be able to query the data in local ONS or in external databases. Factory Method. Defines an interface for creating an object, but let subclasses decide which class to instantiate. This pattern is similar to the abstract factory and can be used also for alternative features. Finally, Observer defines an one-to-many dependency between objects so that when one object changes state, all its dependents are notified and updated automatically. Using this pattern, features can be added to the application as a plug-in, after the deployment. In supply chains, the systems must be flexible the various patterns RFID tags used in identifying of the products or items.

Fig. 5. Simultaneous readings. Adapted (Harrison, 2003) 6.1.2 Optional features Optional features are features that may or may not be present in an application. For this type of feature, three patterns can be used (Gamma et al., 1995): Decorator. Attaches additional responsibilities to an object dynamically. Decorators provide a flexible alternative to sub-classes for extending functionality. The decorator pattern can be used for optional features, mainly those that are additional features. Thus, if a feature is present, the ConcreteDecorator is responsible to manage and call the execution. Prototype. Specifies the kinds of object to create using a prototypical instance, and create new objects by copying this prototype. The prototype pattern specifies the kinds of objects to create using a prototypical instance, and creates new objects by copying this prototype. In this pattern, the prototype specifies how the interaction wit the feature should be, by defining a concrete prototype for each feature. When the EPC Information Service request


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data of any EPC to Object Naming Service and it does not provide information, data obtained of the external databases are copied in local server. Observer. This pattern can be used in the same way as in alternative features. 6.1.3 Or features Or features represent a set of features, from which at least one must be present in an application. For this type of feature, three patterns can be used (Gamma et al., 1995): Bridge. Decouples an abstraction from its implementation so that the two can vary independently. This pattern is appropriated where exist dependence on object representations or implementations, and dependence on hardware and software platform. Builder. The pattern separates the construction of a complex object from its representation so that same construction process can create different representations. This pattern can be used to build composed features. Thus, for the remainder of the architecture, only the Director is available, being responsible to decide which features will be in the application and which will not.. For example, in the transport of pallets, the application decides what transport unit will be utilized (truck, ship, aeroplane, etc), and creates the object automatically considering its characteristics (size, weight, etc). Singleton. Ensures a class only has one instance, and provide a global point of access to it. This pattern also is strongly recommended to or features that interact with mandatory features. 6.2 Component design In this activity, the goal is to create an initial set of interfaces and component specifications. This activity is composed of two steps: Identify Interfaces, and Component Specification. Firstly, is important understand the concept of interfaces. For (Szyperski et al., 2002) define interface as “a set of operations, with each operation defining some services or function that the component will perform for the client”. In concordance with (Cheesman & Daniels, 2000) our work considers two types of interfaces: system and business. The business interfaces are abstractions of the information that must be managed by components. Our process for identifying them is the following: to analyse the feature model to identify classes (for each module and component); to represent the classes based on features with attributes and multiplicity; and refine the business rules using formal language. The system interfaces and their operations emerge from a consideration of the feature model and mainly of the use case model. This interface is focused on, and derived from, system interactions. Thus, in order to identify system interfaces for the components, the domain architect uses the following approach: for each use case, he considers whether or not there are system responsibilities that must be modelled. If so, they are represented as one or more operations of the interfaces (just signatures). This gives an initial set of interfaces and operations. After identifying the interfaces, additional information for specifying components are necessary as, for example, interdependency of the components, and interfaces. The steps presented in this chapter to identifying the interfaces are in concordance with (Cheesman & Daniels, 2000). Firstly, for every component that is specified, the domain architect defines which interfaces its realizations must support (provided and required interfaces). Next, the restrictions of interaction between components must be specified. Unlike the traditional interactions in implementation level, interactions of components define restrictions on the specification level.

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6.3 Architecture views A good way of mapping requirements to implementation is across of the architecture views. The view must be defined as a representation of a set of system elements and the relations associated with them. A view constrains the types of elements, relations and properties that are represented in that view. In this work the four views considered are: (i) module view, (ii) process view, (iii) deployment view, and (iv) data view. The module view shows structure of the system in terms of units of implementation (e.g. component, class, interfaces and their relations). It is essentials because represents the blueprints for software engineering. Despite of the EPCglobal Network to propose one architecture reference, it is can contain different modules in domains. The module view defines three types de relations in concordance with UML relations between modules (Jacobson et al., 1999): is part of, depends, is a as shown the Figure 6. The first relation “is part of” is used when a package contain sub-packages and class. The second relation “depends of” show the dependences between modules, for example, if the EPCIS need to update your data bases are necessary to authenticate in EPCglobal Network. Finally, the relation “is a” have as goal to represent specialization or generalization among modules, or interface realization. The notation more appropriate to represent module view is although UML diagrams as: package, components, class, and objects diagrams. The second architecture view defined in this chapter is the runtime view. It shows the systems in execution, your properties, performance, and help to analyse some features in runtime. The best representation for this view is using the UML diagrams following: Interaction, Timing, State Machine, Activity, Communication, and Sequence diagrams. Together with the activity diagram, the state machine diagram to offer more features to describe the process exists in RFID-based systems of the supply chain. These diagrams depict behavioural features of the system or business process.

Fig. 6. UML Relations between modules In deployment view our goal is to describe the hardware structure which the systems are running. Thus, is possible to verify the interconnection between EPC Information Services,


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to analyse the performance of the EPCglobal Network, security, and access control to data bases. The UML 2.0 define the deployment diagram with goal of shows the physical deployment of the system, such as the computers, and devices (nodes) and how connect to each other. Finally, the data view can be used to describe the data bases modelling and their relationship. The goal this view is to improve performance and adaptability of the systems, and to avoid redundancy and enforce consistency. In RFID-based supply chains context the data view is stronger used to represent the data bases that store information about each RFUD tag. The UML diagram that better show the data view representation is the class diagram. However, this view can also be represented entity-relationship diagram. 6.4 Architecture documentation After defining the view, the domain designer will make the architecture documentation, especially, information that will be applied to more than a vision. In this sense, we define a template with goal of to assist architecture documentation. Architecture Documentation 1. Guidelines Describe the way that the architecture documentation is organized, including the use this document in Supply Chain. 2. Design Information Show design information as, for example, EPC version, previous and auxiliary documents, design members, and goals in general lines. 3. Domain Information Describe the domain that will project their quality attributes, functional and non-functional requirements with major relevance for supply chain designers. 4. Views Documentation 4.1 Name 4.2 Graphic Representation 4.3 Elements Description 4.4 Relationship of views 4.5 Others information 5. Relation between Analysis and Design Show which requirements described in analysis phase are in architecture 6. Glossary Glossary of the system and acronyms

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7. The domain implementation step The last phase of the domain engineering process for RFID-based systems development in supply chain is the Domain Implementation. In concordance with (Pohl et al., 2005), the goal of this step is to provide the implementation and documentation of the reusable assets described in previous step. The activities defined in this chapter for domain implementation step are in concordance with component-based development methods and software reuse processes, among this process are UML Components (Cheesman & Daniels, 2000) and Catalysis (D'Souza & Wills, 1998). The following sections show activities of the domain implementation. 7.1 Component implementation In this activity, the software engineer, based on requirements, implements the software components through a set of well defined sub-activities. The approach is intended to be used in the scope of domain engineering, and therefore it depends on assets developed in domain analysis (feature model, requirements, domain use case model) and domain design (domain-specific software architecture, component specifications). This activity is divided into two sets of sub-activities, each one with a different purpose. Sub-activities 1 to 4 deal with the provided services, i.e. when the software engineer wants to implement a component to be reused. Sub-activities 5 to 7 deal with required services, i.e. when the software engineer wants to reuse services from existent components. The first subactivity is to describe the component, providing general-purpose information, such as the component vendor, version, package, among others. This information may be used to identify a component, an important issue when components are stored in a repository, for example. In this second sub-activity, the software engineer should specify the interfaces. However, as mentioned before, the domain implementation method depends on artefacts developed in domain analysis and design, such as the domain-specific software architecture and component specifications. These artefacts already contain the interface specification, and so the software engineer only needs to review and refine them, if necessary. In the third subactivity, the goal is to implement the services defined in the previous sub-activity, using any implementation technology, as well as the code to register these services to be used by other components, if a dynamic execution environment is used. In fourth sub-activity, which concludes the provided side of the component, the goal is to build and install the component. According to the implementation technology used, this involves compiling and packaging the component in a form that is suitable to be deployed in the production environment. Sub-activities 1 to 4 deal with the provided side of a component. In order to implement the required side, three sub-activities should be performed: First, the software engineer needs to describe the component that will reuse other services. This is similar to first sub-activity, but with the focus on the services that are required. In this sub-activity, the code that accesses the required services is implemented. Here, different techniques can be employed, such as the use of adapters, wrappers, or other ways to implement this access. The main goal of this sub-activity is to provide low coupling between the required service and the rest of the code, so that it can be more easily modified or replaced. The last sub-activity corresponds to building and installing the component that reuses the services, which is similar to fourth subactivity. Although these two sets of sub-activities (1-4 and 5-7) are focused on different


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aspects, in practice they will be present in most components, since normally each component has both provided and required interfaces. 7.2 Component documentation When a component is designed and implemented, the developer has clearly in mind some test scenarios and specifics set of the use cases. Thus, case the client does not encompass the component goals, it will be used incorrect way. In this sense, the component documentation is presented by Sametinger (1997) as “a direct path for information from author to customer, transfers knowledge efficiently. It is one of the most important ways to improve program comprehension [and reduce] software costs”(Sametinger 1997). This way, Kotula (Kotula 1998) presents thirty nine interrelated patterns as solution for documentation of quality. It is grouped in six categories: (i) Generative Patterns: which describe high-level, pattern-creating pattern, (ii) Content Pattern: which describe the material that must be included in the documentation, (iii) Structure Patterns: which describe how the documentation must be organized, (iv) Search Patterns: which discuss the facilities needed to find specific information, (v) Presentation Patterns: describing how the documentation should be presented graphically; and (vi) Embedding Patterns: which provide guidelines for how to embed documentation content within source code. Other the hand, (Taulavuori et al., 2004) says that “definition of the documentation pattern is not sufficient for the adoption of a new documentation practice. An environment that supports the development of documentation is also required”. This way, Taulavuori et al., (2004) provide guidelines concerning how to document a software component. After to analyse patterns defined by Kotula (1998) and component documentation in the context of software product lines, described in (Taulavuori et al., 2004), this chapter defines the following template for component documentation. Component Documentation 1.General Information 1.1 Name Should be well defined and describe the component 1.2 Type Expresses the way the component is intended to be used 1.3 Goal Describe the relation with the RFID technology present in supply chain 1.4 History Describes the life cycle of the component. 2. Interfaces 2.1 Required Interfaces The interface information is here defined as including the interface name, type, description, behaviour and interface functions.

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2.2 Provided Interfaces The same that required interfaces 3. Standards 3.1 Protocol Describe the interaction between two components needed to achieve a specific objective 3.2 Standards What EPCglobal Network standards the component is using? What standards are necessaries? Standards can restrict the compatibility, structure and functionality of components. 4. Technical Details The Technical Details describes details the of design and implementation component. 4.1 Development Environment Defines the environment in which the component has been development. 4.2 Interdependencies Describes component's dependency on the components 4.3 Prerequisites Defines all the other requirements that component may have to operate. 4.4 Implementation Implementation includes composition, context, configuration, and interface implementation. Composition information describes the internal structure of the component, which can be derived from the component's class diagram. The component's class diagram must be included, if possible, as well as the classes, operations and attributes. 4.5 Restrictions Should describe all the items that will limit the provider's options for designing or implementing the components. 5. Information Non-functional 5.1 Modifiability Define as the component can be adapted in new supply chains. 5.3 Expandability Describes how new is often qualified only for OCM components. 5.3 Performance Performance is a quality attribute that measures the component.. The measurements are


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size of the component, prioritisations of events, and utilization in RFID systems. 5.4 Security Define strategies to combat hackers. Including cryptography in RFID tags level. 6. General Information 6.1 Installation guide Defines the operations that must be performed before the component can be used. 6.2 Support Customer support includes the name of the contact person and an address or a phone number where the customer can get help.

8. Conclusion As widely discussed in this chapter, the reuse process present gaps and lack of details in key activities such as, for example, domain engineering. In this sense, we believe that our approach can be useful to reduce the gaps and lack of details among the steps, and presenting a domain engineering process for RFID-based systems development presents in supply chain domain. This work can be seen as initial climbing towards the full vision for an efficient domain engineering process and interesting directions remain to improve what started and to explore new routes. Thus, the following issues should be investigated as future work: (i) Other RFID Standards, (ii) Other Directions in Software Architecture, for instance, Service-oriented or Model-Driven and (iii) Architecture Documentation.

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Bauer, D. (1993). A Reusable Parts Center, IBM Systems Journal, Vol. 32, No. 04, pp. 620-624, September, 1993 Boehm, B. W. (1988). A Spiral Model of Software Development and Enhancement, IEEE Computer, Vol. 21, No. 05, pp. 61-72, May, 1988 Bosch, J. (2000). Design and Use of Software Architecture, Addison-Wesley, 2000, pp. 354 Campos, L. B. & Zorzo, S. D. (2007). A Domain Analysis Approach for Engineering RFID Systems in Supply Chain Management, IEEE International Conference on System of Systems Engineering, Texas, USA, pp. 165-171, April, 2007, San Antonio Cheesman, J. & Daniels, J. (2000). UML Component A Simple Process for Specifying Component-Based Software, Addison-Wesley, 2000, pp. 208 Clements, P & Northrop, L. (2001). Software Product Lines: Practices and Patterns, AddisonWesley, 2001, pp. 608 Clements, P. et al., (2004). Documenting Software Architectures: Views and Beyond, Addison-Wesley, 2004, pp. 512 Czarnecki, K & Eisenecker, U. W. (2000). Generative Programming: Methods, Tools, and Applications, pp. 832, Addison-Wesley, 2000 Dikel, D. (1997). Applying Software Product-Line Architecture, IEEE Computer, Vol. 30, No. 08, pp. 49-55, August, 1997 D'Souza, D. F. & Wills, A. C. (1998). Objects, Components and Frameworks with UML: The Catalysis Approach, Addison-Wesley, 1998, pp. 816 Endres, A. (1993). Lessons Learned in an Industrial Software Lab, IEEE Software, Vol. 10, No. 05, pp. 58-61, September, 1993 Engels, D (2003). The use of the Electronic Product Code, White Paper of the Massachusetts Institute of Technology (MIT), pp. 03, February 2003. EPCglobal (2005). Object Naming Service (ONS) Version 1.0, EPCglobal Ratified Specifications, pp. 09, October, 2005 Ezran, M.; Morisio, M & Tully, C. (2002). Practical Software Reuse, pp. 374, Springer Frakes, W. B. & Isoda, S. (1994). Success Factors of Systematic Software Reuse, IEEE Software, Vol. 12, No. 01, pp. 15-19, September, 1994 Frakes, W. B. & Kang, K. C. (2005). Software Reuse Research: Status and Future, IEEE Transactions on Software Engineering, Vol. 31, No. 07, pp. 529-536, July, 2005 Gamma, E. (1995). Design Patterns: Elements of Reusable Object-Oriented Software, Addison-Wesley, 1995, pp. 395 Gomaa, H. (2005). Designing Software Product Lines with UML: From Use Cases to PatternBased Software Architectures, Addison-Wesley, 2005, pp. 701 Griss, M. L. (1994). Software Reuse Experience at Hewlett-Packard, 16th IEEE International Conference on Software Engineering, pp. 270, Italy, May, 1994, Sorrento Griss, M. L. (1995). Making Software Reuse Work at Hewlett-Packard, IEEE Software, Vol. 12, No. 01, pp. 105-107, January, 1995 Harrison, M. (2003). EPC Information Service – Data Model and Queries, White paper of the Massachusetts Institute of Technology (MIT), pp. 03, October, 2003 Jacobson, I.; Booch, G. & Rumbaugh, J. (1999). The Unified Software Development Process, Addison-Wesley, 1999, pp. 463 Joos, R. (1994). Software Reuse at Motorola, IEEE Software, Vol. 11, No. 05, pp. 42-47, September, 1994


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7 Return Policies and Coordination of Supply Chain Mabel C. Chou

Department of Decision Sciences National University of Singapore, Singapore 1. Introduction In many industries, manufacturers reply upon independent retailers to distribute their products as manufacturers can benefit from retailers’ reputations, economies of scale, and knowledge about local markets. However, manufacturers and retailers being independent agents act in their own best interest. A supply chain composed of independent agents acting in their own best interests will generally not achieve system-wide efficiency, often due to some incongruence between incentives faced locally and the global optimization problem, a phenomenon known in the economics literature as “double marginalization”. (Spengler 1950, Tirole 1988). To efficiently use the supply chain one needs some mechanisms to coordinate supply chain to maximize total channel profit. Coordination of the supply chain implies that the profit of the supply chain is maximized, hence achieving system-wide efficiency. One way to coordinate channel decisions is by using a returns policy provided by the manufacturer. This paper is organized as follows. Section 2 presents an overview of the benefits and costs of returns policies. Section 3 reviews the different kinds of returns policies that are required to coordinate the supply chain for the different types of products. Section 4 discusses how returns policies will be affected by demand uncertainty and Section 5 studies the impact that demand uncertainty together with retailing competition has on returns policies. Section 6 provides a conclusion of the paper.

2. Overview of return policies A returns policy for excess inventory is a commitment made by a manufacturer, or distributor upstream when accepting products from a downstream channel member. The most generous policy promises to refund the full wholesale price for all returned products, while less generous policies offer credits against future orders. A partial returns policy gives only partial credits or refunds. (Padmanabhan & Png 1995) The format of returns policies varies in and across industries. Manufacturers and distributors across a wide range of products have long allowed retailers to return excess inventory. Today, returns policies are common in the distribution of books, magazines and newspapers, recorded music, computer hardware and software, greeting cards and pharmaceuticals. (Padmanabhan & Png 1995)

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2.1 Benefits of returns policies At the most superficial level, returns policies will encourage retailers to carry larger stocks. From the manufacturer’s standpoint, the more retailers sell, the higher the manufacturer’s profit will be. Returns policies are thus beneficial to both the manufacturers and retailers. Below is a discussion of the benefits to the retailers. 2.1.1 Mitigate retailers’ risk Uncertain product demand is the reason for retailers’ reluctance to carry more stock as they fear having excess inventory. By stocking conservatively, retailers limit the manufacturer’s potential sales. The manufacturers can mitigate the retailers’ risk of having excess inventory by offering returns policies which will encourage the retailers to stock more products and hence increase the sales of the manufacturers. (Padmanabhan & Png 1995) 2.1.2 Safeguard the brand The institution of returns policies will discourage retailers from marking down the price of the product when it is nearing the expiration date and selling of stale products. Returns policies are a more attractive way of dealing with expiring products and will help to safeguard the manufacturer’s brand. (Padmanabhan & Png 1995) 2.1.3 Support end-user returns policy The institution of return policies will be beneficial to retailers as it allows them to return products to the manufacturers when they are pressured to accept returns from their own customers. End users want to be able to return products to retailers to safeguard against the risk that the product will not satisfy them. (Padmanabhan & Png 1995) 2.1.4 Facilitate distribution of new product information Returns policies may aid information transmission between manufacturers and retailers more effectively. A manufacturer that is sure its products will sell well can afford to offer a generous returns policy, knowing that retailers will not return the items as explained by Chu (1993). Hence a returns policy is one way a new manufacturer can credibly signal information about expected market demand and product quality. (Padmanabhan & Png 1995) Since returns policies also place the consequence of product failure on the manufacturers, the incentives of the manufacturers and retailers are thus aligned. As a result, a returns policy is also a way the manufacturer can commit to investments in advertising, promotion and other activities to enhance product sales. (Padmanabhan & Png 1995) Returns policies also serve as forms of assurance to the retailers that a manufacturer will not bring out new products so quickly that the retailers’ stock will be obsolete. If the manufacturer does so, retailers can return the superseded items, hence punishing the manufacturer. (Padmanabhan & Png 1995) 2.1.5 Structure competition A returns policy strengthens a manufacturer’s position relative to other competing brands as it reduces the cost of carrying excess inventory and tilts the balance in the retailers’ mind towards carrying larger stocks of the manufacturer’s product. As a result, the probability of a stock-out and of consumers switching to competing brands will be lower. Hence the

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institution of returns policies has strategic implications for manufacturer’s profits by having an impact on competition among retailers and brands. (Padmanabhan & Png 1995) 2.2 Costs of returns policies When some of the additional stocks are returned, a returns policy generates additional costs for the manufacturer which will be discussed below. 2.2.1 Logistics costs One of the costs incurred by a returns policy is the logistic costs of organizing, packing, and shipping of products back and forth. Depreciation cost is another cost that the manufacturer incurred on returned items. Returned items depreciate as they may be damaged in shipment, decay physically or lose their marketability over time. (Padmanabhan & Png 1995) 2.2.2 Demand uncertainty The demand for products such as new books, CDs, software and pharmaceuticals is uncertain. Since a returns policy transfers the cost of excess stocks from retailers to the manufacturer, the more uncertain demand is, the greater the cost of a returns policy to the manufacturer. (Padmanabhan & Png 1995) 2.2.3 Retailer incentives By reducing the risk of losses due to excess inventory, a returns policy lessens some of the retailer’s incentive to invest in efforts to promote retail sales by merchandising, doing pointof-sale advertising and providing attractive shelf space. (Padmanabhan & Png 1995) Hence, when manufacturer accepts the risk from the retailer, the retailer’s incentive to invest in promotional efforts may be dulled and this is a cost to the manufacturer. 2.3 Implementation of returns policies The partial returns policy which rebates only part of the wholesale price for return items is most widely implemented to address the retailers’ incentive to overstock and avoid pointof-sale marketing efforts. In this way, the manufacturer and the retailer share the risk, hence providing some incentives for all parties to play their part. Partial risk gives the manufacturer an incentive to support the product and to select new introductions carefully while partial risk gives retailers the incentive to order conservatively and promote the product. Ultimately, the aim of the partial returns policy is to coordinate the supply chain to maximize total channel profit. Some other kinds of partial returns policy are those with a time limit for returns as well as those which take into consideration a retailer’s returns history into decisions on pricing, credit and even on whether to continue dealing with the retailer. A time-consuming and expensive way for a manufacturer to devise returns policies for a mix of retailers that differ in risk aversion, competitiveness and skepticism is to tailor a returns policy for each retailer. Another way is to design a menu of alternative returns policies such as more generous returns policies with higher wholesale prices or strict limits on returns with lower wholesale prices and allow every retailer to choose among the options. (Padmanabhan & Png 1995)

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3. Types of products This section will investigate the different kinds of returns policies that are required to coordinate the supply chain for the different types of products, namely perishable commodities, style goods, catalogue style goods, experience goods, Internet sales and buildto-order products. 3.1 Perishable commodities Perishable commodities are short life-cycle items with limited shelf or demand life such as newspapers, baked goods, periodicals and records. An earlier investigation of employing a returns policy for perishable commodities to coordinate the distribution channel appeared in Pasternack (1985) where two extreme cases under the assumption of risk-neutral supply chain members are analyzed. It is impossible to achieve channel coordination if the manufacturer allows the retailer full credit for all unsold perishable goods or no returns for any unsold perishable goods. On the other hand, if the manufacturer offers retailers full credit for a partial return of goods this may achieve channel coordination in a single-retailer environment. Since the optimal return allowance will be a function of retailer demand, such a returns policy cannot be optimal in a multi-retailer environment. In contrast, he shows that a returns policy in which a manufacturer offers a partial credit for all unsold goods can achieve channel coordination in a multi-retailer environment. 3.2 Style goods Style goods are characterized by highly uncertain demand, long production lead time and have little or no salvage value at the end of their short selling seasons of a few weeks or months. They include products such as computer software, hardware, compact discs, fashion items, greeting cards, books and pharmaceuticals. Mantrala and Raman (1999) study how the supplier’s wholesale-buyback price policy influence the retailer’s optimal order quantity decision with different levels of demand variability as well as study how demand uncertainty the profitability of both the suppliers and retailers. With demand variability remaining constant, supplier can induce the retailer to purchase more stock by increasing the buyback price and/or reducing the wholesale price. When demand uncertainty increases, the supplier tends to drop his optimal buyback price even though the buyer is ready to increase her order quantity at any given wholesale price. This indicates that the supplier finds that his expected costs of returns at the higher buyback price are too high relative to his expected revenues. In addition, at any given wholesale price level, the retailer's total profits as well as the total system profits fall as demand variability increases. On the other hand, at a very low wholesale price, the supplier does not find it optimal to accept returns at all and hence his optimal buyback price is zero. However, the buyer is still willing to order larger amount of stocks at higher levels of uncertainty even without a buyback price offer provided the wholesale price is sufficiently low. Hence, increasing demand uncertainty works completely in favor of the supplier in this situation and thereby improves his expected profits. However, at the higher wholesale price, the supplier has to offer a large buyback price which significantly increases his expected costs of returns from the retailer, and thereby lowers his expected profits as demand uncertainty increases.


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Finally, the supplier makes his largest profits at high wholesale prices accompanied by high buyback prices. With the right combination he can make the retailer purchase as much style goods as possible at a much lower wholesale price and no buyback price. 3.2.1 Catalogue style goods Catalogue refers to a particular kind of style goods in which a retailer advertises an item at a particular price in a catalogue that is distributed to customers. Since cost considerations prohibit the frequent distribution of catalogues, the retailer must commit to a single price for an item for the entire selling season. With a price-dependent demand model, Emmons and Gilbert (1998) show that under demand uncertainty, a supplier using a returns policy for catalogue style goods to repurchase excess stock from the retailer at the conclusion of the period can improve the profits if demand follows a uniform distribution. They also find that there exists at least a wholesale price where returns policy is a ‘win–win’ strategy for retailer and supplier. The offer to buy back excess stock tends to increase the total combined profits of the retailer and manufacturer; hence it is not a zero-sum game. A manufacturer can "buy" the loyalty of a retailer relatively cheaply by decreasing the wholesale price by a small amount. It is always in the retailer's interest to have the manufacturer buy back unsold items at the end of the season, and the manufacturer also benefits from such arrangements when wholesale prices are sufficiently large. However, when the manufacturer sets his wholesale price optimally, he gains more from the offer of a returns policy than does the retailer. 3.3 Experience goods Experience goods are goods for which idiosyncratic valuations such as buyer’s remorse are possible only after purchase. This is because customers do not fully know their preferences for the products at the time of purchase, but after they gain some experience with them. Returns policies for experience goods allow customers to defer their purchasing decisions until after gaining some experience with the products. A consumer who has learned that he does not like a product can cancel his purchase by simply returning it. These returns policies do not require customers to provide evidence or an explanation regarding the malfunction of the returned good. Instead, a customer not liking a product is often a sufficient reason for stores to accept the return. The "no-questions-asked" full refund policy is customary with many big retailers. According to Che (1996), returns polices for experience goods insure consumers against ex post loss, which allow a monopoly seller to charge a higher price. This is because under the returns policy, consumers can return the good after learning their valuations, at zero cost. However, the seller cannot induce consumers to buy at a price above their ex post valuations with a no-returns policy. This is because under the no-returns policy, consumers bear the entire risk associated with their uncertain ex post valuation. As risk aversion increases, the seller must lower her price to compensate consumers for the risk. Che (1996) also demonstrates that the returns policy is optimal if the consumers are sufficiently risk averse. This is because the returns policy eliminates a consumer's risk of paying more than his ex post valuation; hence the marginal consumer is not adversely affected by risk aversion. He also shows that returns policy is optimal if the retail costs are high. When the retail costs are high, the screening opportunity of the returns policy: seller can charge a high price and sell only to high-valuation consumers is relatively more

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valuable since the seller can maintain her profit margin by selling only to high-valuation consumers. Furthermore, superior risk sharing makes consumers strictly better off under the returns policy as the consumers are protected from any loss and so they receive strictly positive expected utility. However, the seller's failure to internalize this benefit leads to too little adoption of the returns policy in equilibrium. 3.4 Internet sales The e-business revolution in recent time has brought an alternative model for the part of the supply chain from the manufacturer to the customer. More and more manufacturers are now attempting to sell directly to the customers bypassing the traditional distributorwholesaler-retailer chain. Their motivation for this is to reduce the distribution cost and be more responsive to customers’ requirements. Customers also view Internet purchase as advantageous because it drastically reduces the search cost and is convenient due to the fact that the store is open 24 hours per day seven days a week. In an Internet direct sales supply chain, the customers buy direct from the manufacturer, hence sacrificing the benefits of physical inspection of the product. This increases the probability that the customers will be dissatisfied with the product and likely to return it. Hence, a common customer’s concern is the lack of a proper returns policy for internet purchase and the complicated logistics for returning an item. As such, a clearly explained and generous returns policy will be welcomed by the customers and therefore will enhance demand. From the manufacturer’s point of view, a generous returns policy will increase customers’ confidence and hence increase sales revenue by inducing more customers to buy. On the other hand, returns policies would increase the cost of business substantially due to increased likelihood of return. Hence, returns policy constitutes a tradeoff and an optimum policy would be one whereby the resultant profit would be maximized. (Mukhopadhyay and Setoputro 2004) The returns policy practice in e-business varies across industries and stores, and may range from unconditional money back guarantee to store credit only to no refund. Mukhopadhyay and Setoputro (2004) find out that in a market where customers are less pricesensitive, the e-tailer can offer a more generous returns policy and at the same time will be able to charge higher price. Optimum pricing and returns policies will generate higher demand for the product but the e-tailer will also see higher return quantity. Overall, profit can be maintained at a higher level because the extra revenue from charging higher price and from increased sales outweighs the increase in cost due to increase in return quantity. In addition, they also find out that in a market where customer’s demand is increasingly more sensitive to the returns policy, the optimum price will increase and the e-tailer can offer more generous returns policy to increase sales. Although offering more generous returns policy will also increase return quantity, the e-tailer’s profit will not be reduced. This is because when customer is more sensitive to returns policy, e-tailer can charge higher price to offset the cost increase due to offering more generous returns policy. Results from the paper also show that when the customer is less sensitive to the rate of return parameter, offering generous or restrictive returns policy will not make much difference. Hence, the e-tailer can offer more generous returns policy without affecting profit level. This can be observed in the arts industry where the customers are less sensitive to the rate of return parameter and so sellers generally offer more generous returns policy. On the other hand, when customer is sensitive to the rate of return parameter, e-tailer tends


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to offer more restrictive returns policy because e-tailer is afraid that customers may abuse their returns policy. This can be seen in the electronic and apparel industries where the customers are widely known as sensitive to the rate of return parameter and so sellers often impose less generous returns policy on their customers. 3.5 Build-to-order products A build-to-order product (BTO) is essentially built to customize the product to the requirement of the customers, hence increasing both lead time and cost of production. Firms are generally reluctant to offer a returns policy because the returned merchandise is practically useless as it was designed to meet the requirement of a particular customer. Hence returns policy will not be suitable in case of a BTO product with almost zero salvage value. Mukhopadhyay and Setoputro (2005) propose the concept of modularity in the institution of returns policies for BTO products. When a BTO product with modular design is returned, the product can be dismantled very easily producing a number of components which are standard products keeping their full value. This returned product will give back a large salvage value to the firm, thereby cutting down its loss due to the return. Hence, the company will incur a lower cost of return from the returned product when the level of modularity is higher. They also demonstrate that in the market where customer demand is more sensitive to the returns policy, the seller will offer more generous returns policy and simultaneously the optimum modularity level shall be increased. This is because since the BTO product is customized, offering a generous returns policy without increasing the modularity level will increase the cost of returning. In addition, in the market where the demand is increasingly more sensitive to the modularity level, the optimum modularity level will increase and the seller shall simultaneously offer more generous returns policy. Moreover, when the seller can decrease their product development and design costs, the optimum modularity level will increase and at the same time the firm can offer more generous returns policy. Furthermore, when the seller can salvage more from the returned product, the optimal returns policy and the optimum modularity level offered will increase. Generous returns policy is favorable when the retailer can obtain high salvage value for returned merchandise.

4. Demand uncertainty This section will discuss how return policies will be affected by demand uncertainty. Manufacturers whose products are subject to uncertain demand face a problem of inducing distributors to stock those products. A manufacturer may attempt to compensate its distributors by agreeing to accept returns of unsold goods for full or partial refunds of their purchase price. 4.1 Nature of demand uncertainty Marvel and Peck (1995) show that the manufacturer’s decision to accept returns depends crucially on the nature of demand uncertainty. Two cases of demand uncertainty are discussed in their paper. Valuation uncertainty occurs when firms are unsure about the willingness of customers to pay for the manufacturer’s product while arrival uncertainty occurs when firms do not know how many customers will arrive. When uncertainty applies

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only to the valuation that consumers place on the manufacturer’s product, but not to arrival uncertainty, returns policies distort pricing without offsetting the inventory efforts. Prices are forced up while the expected quantity sold remains low and hence returns policies are not employed in the case of valuation uncertainty. On the other hand, if the manufacturer and retailer have a good idea about the amount consumers will be willing to pay for the product, but do not know how many consumers will arrive at the marketplace, a high return allowance is attractive to the manufacturer. In addition, the authors demonstrate that when arrival uncertainty is small relative to valuation uncertainty, the manufacturer chooses not to permit returns. However, increases in the arrival uncertainty parameter result in the institution of returns policies and the ratio of the returns to wholesale price rises rapidly with arrival uncertainty. Marvel and Peck (1995) also point out that return allowances are far more widespread in Japan than in United States. The fragmented nature of Japanese distribution into smaller units as compared to those in the West is a consequence of legal obstacles to construction of large stores. Distributing sales over a larger number of outlets will increase the arrival uncertainty at each individual outlet relative to the sales at that outlet. However, valuation uncertainty is unlikely to be affected at any individual outlet. As a result, the liberal return policies of Japanese manufacturers are a profit-maximizing adaptation to the fragmented nature of Japanese distribution. 4.2 Multi-store retailer Mantrala and Raman (1999) study the impact of demand uncertainty on supplier’s returns policies for a multi-store style-good retailer. In their research the retailer, assumed to be a central department store, had two different retail outlets whose demand may or not have been correlated and the retail outlets are not in competition. For any given non-optimal wholesale price and returns value, they numerically study how demand variability affected suppliers’ and retailers’ profits and return credits. At any given wholesale price, the supplier tends to drop his optimal buyback price as demand uncertainty increases even though the buyer is ready to increase her order quantity. On the other hand, at a very low wholesale price, the supplier in fact does not find it optimal to offer to accept returns at all; hence his optimal buyback price is zero. However, the supplier makes his largest profits at high wholesale prices accompanied by high buyback prices. 4.3 Multi-item returns policy Brown, Chou and Tang (2008) study a multi-item returns policy called “pooled” (or joint) returns policy under which the distributor can return any combination of the products up to R percent of the total purchases across all products. They analyze the distributor's optimal profit and order quantity under the pooled returns policy, and compare these operating characteristics to the case when a single-item “non-pooled” returns policy is instituted. Under the non-pooled returns policy, the distributor can only return on individual items using item-specific return limits. Under the non-pooled policy, the distributor can return each product separately up to R percent of the purchase of that product. They show that the distributor will always achieve a higher profit under the pooled policy. They also show that the manufacturer could actually obtain a lower profit under the pooled policy due to a counter-intuitive result: the distributor may order less under the pooled policy even though the pooled policy offers more flexibility. This counter-intuitive result motivates them to determine the conditions under which the distributor would order less under the pooled policy. Finally, they develop


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a heuristic for determining the distributor's optimal order quantities associated with the nproduct case under the pooled policy.

5. Demand uncertainty with retail competition The papers by Marvel and Peck (1995) and Mantrala and Raman (1999) only consider the effect of demand uncertainty on returns policies. In this section, we will study the impact that demand uncertainty together with retailing competition have on returns policies. Padmanabhan and Png (1997) point out that when retailing is competitive but demand is certain, a returns policy intensifies retail competition and reduces the retailer margins, hence benefiting the manufacturer. On the other hand, when retailing is a monopoly while demand is uncertain, a returns policy helps the manufacturer by intensifying retail distribution, but hurts by encouraging excessive stocking. They also demonstrate that when there are both competing retailers and demand is uncertain; there is a trade-off between benefits of more intense retail competition and the costs of excessive stocking of a returns policy. As a result, the manufacturer shall adopt a returns policy if the marginal production cost and the demand uncertainty are sufficiently low. The lower the marginal cost and demand uncertainty, the greater will be the benefit from more intensive retail competition and the smaller will be the manufacturer’s loss from excessive stocking. Since the marginal costs of production of books, CDs and computer software are small in comparison to their price, returns polices are common in the distribution of books, recorded music and software. In addition, Yao, Wu and Lai (2005) study how demand variability and retailer’s competition interacts with manufacturers’ return prices in influencing retailer decisions on order size and retail price, and the implications for manufacturers’ policies and profitability of the parties in the supply chain. They point out that a returns policy always benefits the manufacturer. If the demand uncertainty and wholesale price are very high, the best decision of manufacturer is to provide either a return credit or full returns policy. However, channel profits, and particularly the expected profits of the retailers, may not fare well under this policy. If the manufacturer provides a lower wholesale price, the optimal decision of the manufacturer is not to provide any returns credit. They also demonstrate that the competing power of the retailer has an impact on the distribution channel members’ decisions. Intensifying the competition between two duopoly competing retailers will lead to a decline in both the retailers’ and the channel expected profits. In contrast, supplier’s expected profits are increasing as the supplier’s wholesale price increases with his setting the optimal returns price under all scenarios. The supplier’s action of setting high wholesale price leads to a high equilibrium retail price that in turn leads to a decline in market demand, thus resulting in a decrease in the retailers’ expected profits. Hence, although a returns policy can induce the retailer to order more so that the supplier’s profits and channel profits improve, the retailer’s expected profits are destroyed significantly. Furthermore, increasing the demand uncertainty works completely in favor of the supplier in this situation. On the other hand, in the higher uncertain demand situation, the retailers should adopt a risk-averse policy so that they can share more total profits. Furthermore, they find out that the price sensitivity factor has a significant impact on the returns policy. With low price sensitivity, the manufacturer does not generally adopt a returns policy, particularly in low demand uncertainty. In contrast, with high price sensitivity, the manufacturer will like to adopt a returns policy to improve his profits. However, the returns policy requires the support of the high wholesale price, which leads to the severe cannibalization of the retailers’ profits.

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6. Conclusion We have come to the concluding section of this literature review. A returns policy provided by the manufacturer can coordinate the supply chain to maximize total channel profit. Any manufacturer may find that the benefits and costs of a returns policy pull in different directions and whether a manufacturer should accept returns depends on the balance between the benefits and costs. We have also looked at the different kinds of returns policies that are required to coordinate the supply chain for the different types of products, namely perishable commodities, style goods, catalogue style goods, experience goods, Internet sales and build-to-order products. In addition, we also study the impact of these return policies on the optimal pricing of the manufacturer and optimal order quantity decision of the retailer. We also discuss the impact that demand uncertainty alone have on return policies and extend our study to include the impact of both demand uncertainty and retailing competition on returns policies.

7. References Brown, A., Chou, M.C., Tang, C.S., 2008. The Implications of Pooled Returns Policies. International Journal of Production Economics, 111, 129-146. Che, Y.-K., 1996. Customer Return Policies for Experience Goods. Journal of Industrial Economics, 44, 17-24. Chu, W., 1993. Demand Signaling and Screening in Channels of Distribution. Marketing Science, 11, 327-347. Emmons, H., Gilbert, S.M., 1998. Note: The Role of Returns Policies in Pricing and Inventory Decisions for Catalogue Goods. Management Science, 44 (2), 276-283. Lai, K.K., Wu, Y., Yao, Z., 2005. Demand uncertainty and manufacturer returns policies for style-good retailing competition. Production Planning & Control, 16(7), 691-700. Mantrala, M.K., Raman, K., 1999. Demand uncertainty and supplier’s returns policies for a multi-store style-good retailer. European Journal of Operational Research, 115, 270– 284. Marvel, H.P., Peck, J., 1995. Demand Uncertainty and Returns Policies. International Economic Review, 36(3), 691–714. Mukhopadhyay, S.K., Setoputro, R., 2004. Reverse logistics in e-business optimal price and return policy. International Journal of Physical Distribution & Logistics Management, 34(1), 70-88. Mukhopadhyay, S.K., Setoputro, R., 2005. Optimal return policy and modular design for build-to-order products. Journal of Operations Management, 23, 496-506. Padmanabhan, V., Png, I.P.L., 1995. Returns policies: Make money by making good. Sloan Management Review, Fall, 65-72. Padmanabhan, V., Png, I.P.L., 1997. Manufacturer's Returns Policies and Retail Competition. Marketing Science, 16 (1), 81-94. Pasternack, B.A., 1985. Optimal Pricing and Return Policies for Perishable Commodities. Marketing Science, 4(2), 166–176. Spengler, J. J. 1950. Vertical restraints and antitrust policy. J. Political Economics, 58, 347– 352. Tirole, J. 1988. The Theory of Industrial Organization. MIT Press, Cambridge, MA.

8 Operational Management of Supply Chains: A Hybrid Petri Net Approach Mariagrazia Dotoli, Maria Pia Fanti and Agostino Marcello Mangini Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari Bari, Italy

1. Introduction The emergence of Supply Chains (SCs) is an outcome of the recent advances in logistics and information technology. SCs are complex networks interconnecting different independent manufacturing and logistics companies integrated with material, information and financial flows (Viswanadham & Raghavan, 2000). Typically, SC management decisions are classified into three hierarchical levels according to the time horizon of decisions: strategic (longterm), tactical (medium-term), and operational (short-term, real-time) (Chopra & Meindl, 2001; Shapiro, 2001). Accordingly, different models have to be defined at each level of the decision hierarchy to describe the multiple aspects of the SC. While the development of formal models for SC design at strategic and tactical levels was addressed in the related literature (Dotoli et al., 2005; Dotoli et al., 2006; Gaonkar & Viswanadham, 2001; Luo et al., 2001; Vidal & Goetschalckx, 1997; Viswanadham & Gaonkar, 2003), research efforts are lagging behind in the subject of modeling and analyzing the SC operational performance. At the operational level, SCs can be viewed as Discrete Event Dynamical Systems (DEDSs), whose dynamics depends on the interaction of discrete events, such as customer demands, departure of parts or products from entities, arrival of transporters at facilities, start of assembly operations at manufacturers, arrival of finished goods at customers etc (Viswanadham & Raghavan, 2000). Among the available DEDS analytical formalisms, Petri Nets (PNs) may be singled out as a graphical and mathematical technique to model systems concurrency and synchronization. Moreover, PNs are able to capture precedence relations and structural interactions and may be executed in standard engineering software packages simply implementing their dynamics via the corresponding matrix equations. However, most SC models based on PNs proposed in the related literature share the limitation that products are modelled by means of discrete quantities, called tokens (Desrochers et al., 2005; Dotoli & Fanti, 2005; Elmahi et al., 2003; Viswanadham & Raghavan, 2000; Von Mevius & Pibernik, 2004; Wu & O’Grady, 2005). This assumption is not realistic in large systems with a huge amount of material flow: the state space of the SC model generally turns out to be excessively large, so that inconveniences in the simulation and performance optimization often arise, leading to large computational efforts. Since SCs are DEDSs whose number of reachable states is very large, PN formalisms using fluid approximations provide an

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aggregate formulation to reduce the state space dimension (Alla & David, 1998; Silva & Recalde, 2004). Hence, hybrid PNs may be employed to describe SCs efficiently and effectively at the operational level. In this context, First Order Hybrid Petri Nets (FOHPNs) (Balduzzi et al., 2000) are an emerging formalism. This chapter focuses on SC operational management: we employ a FOHPN model recently proposed by the authors (Dotoli et al., 2008) and implement and compare two standard operational management strategies. The model is built by a modular approach based on the bottom-up methodology (Zhou & Venkatesh, 1998): manufacturers are described by continuous transitions, buffers are continuous places and products are continuous flows routing from manufacturers, buffers and transporters. Transporters are stochastic transitions with a triangular distribution for the transportation time. Moreover, discrete places and transitions describe the financial and information flows that enable, inhibit or change the material flow. Discrete exponential transitions model information about demands and occurrence of unpredictable events, e.g. the blocking of a supply or an accident in a transportation facility. The FOHPN model allows us to address two issues: system management and optimal mode of operation. The SC management is realized by PN structures that synthesize two well-known policies, namely Make-To-Stock and Make-ToOrder, and standard inventory control rules. While the SC optimization models presented in the related literature determine the decision parameters off-line (e.g. see Gaonkar & Viswanadham, 2001; Vidal & Goetschalckx, 1997; Viswanadham, 1999; Viswanadham & Gaonkar, 2003) in order to design and manage the SC, the task of the considered model is selecting some operational SC parameters in a short time, based on knowledge of the system state and of the occasional uncontrollable events. More precisely, the optimal mode of operation is obtained by the computation of control variables, i.e., the instantaneous firing speeds of continuous transitions, solving a linear programming problem optimizing a chosen performance index. A representative SC example shows the technique effectiveness under the two operational management policies and by a standard inventory control rule, considering in each case a different optimal operative condition. Simulation results show that the selected formalism leads to an effective SC operational management, as well as to the possibility of choosing important system control parameters, e.g. the production rates. Future research includes implementing additional management policies and inventory rules in the chosen formalism.

2. The system description 2.1 The SC structure The SC structure is typically described by a set of facilities with materials that flow from the sources of raw materials to subassembly producers and onwards to manufacturers and consumers of finished products. Moreover, feedback paths may be present if demanufacturers or recyclers are included in the SC. The SC facilities are connected by transporters of materials, semi-finished goods and finished products. More precisely, the entities of a SC are the following: 1. Suppliers: a supplier is a facility that provides raw materials, components and semifinished products to manufacturers that make use of them. 2. Manufacturers and assemblers: manufacturers and assemblers are facilities that transform input raw materials/components into desired output products.

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3.

Distributors: distributors are intermediate nodes of material flows representing agents with exclusive or shared rights for the marketing of an item. 4. Retailers or customers: retailers or customers are sink nodes of material flows. 5. De-manufacturers or recyclers: entities of the de-manufacturing stage feed recovered material, components or energy back to suitable upstream SC facilities. 6. Logistics and transporters: storage systems and transporters play a critical role in distributed manufacturing. The attributes of logistics facilities are storage and handling capacities, transportation times, as well as operation and inventory costs. Here, part of the logistics, such as storage buffers, is considered pertaining to manufacturers, suppliers and customers. Moreover, transporters connect the different stages of the production process. The SC dynamics is traced by the flow of products between facilities (i.e., entities of types 15) and transporters (i.e., entities of type 6). Because of the large amount of material flowing in the system, we model a SC as a hybrid system: the continuous dynamics models the flow of products in the SC, the manufacturing and the assembling of different products and its storage in appropriate buffers. Hence, resources with limited capacities are represented by continuous states describing the amount of fluid material that the resource stores. Moreover, we consider also discrete events occurring stochastically in the system, such as: a. the blocking of the raw material supply, e.g. modeling the occurrence of labour strikes, accidents or stops due to the shifts; b. the blocking of the transport operations due to the shifts or to unpredictable events such as jamming of transportation routes, accidents, strikes of transporters etc.; c. the start of a request from the retailers. 2.2 An example of SC We describe an example of SC whose target product is a desktop computer system (Dotoli et al., 2008), inspired from a case study reported in (Dotoli et al., 2006; Luo et al., 2001). Figure 1 depicts the SC network, comprising three suppliers, two manufacturers, one distributor, two retailers and one de-manufacturer. Moreover, twelve transporters connect the facilities. Each edge represents the flow of the material and is labelled by the parts/products that are transported between the connected facilities: the Personal Computer or PC, the central processing unit or C, the hard disk driver or H, the keyboard or K and the monitor or M. In particular, the last four types of products are semi-finished products obtained by the suppliers S1, S2 and S3, while the PC is produced by manufacturer M1 (M2) with a bill of materials of C, H, M and K obtained from suppliers S1 and S2 (S3). Moreover, retailers R1 and R2 obtain from distributor D1 the product PC. In addition, the de-manufacturer DM1 obtains the finished product PC from the retailers and supplies manufacturer M1 (M2) with the semi-finished product H (C). Note that the SC scheme includes two inter-twined productive chains with a remarkable advantage: if a transportation link is temporarily unavailable the productive cycle does not stop. 2.3 SC management and inventory control rules The operational SC dynamics depends on the considered planning and management methodology, which specifies the business model and determines the paths for the information and material flow in the SC, and on the corresponding inventory control rules governing each SC facility (Viswanadham & Raghavan, 2000).

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Fig. 1. The structure of the considered example SC.



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According to the Wortmann classification (Wortmann, 1983), three SC managing policies are followed in practice: Make-To-Stock (MTS), Make-To-Order (MTO) and Assemble-To-Order (ATO) or Build-To-Order (BTO). In particular, in order to deliver on time the produced goods to end-users, the MTS strategy governs the system initiating production before the actual occurrence of demands, so that end customers are satisfied from stocks of inventory of finished goods. Often, under MTS management, stocks in the SC are governed by a reorder point based policy and inventory is replenished as soon as a preset reorder level is reached, so that the target level is maintained. On the other hand, in the MTO technique customer orders trigger the flow of materials and the requirements at each production stage of the SC. Hence, under such a management strategy, a lower level of material and product inventory is maintained. Furthermore, the ATO or BTO policy can be viewed as a hybrid of the former two strategies, basically applying MTS in the first stages of the SC and MTO in the last stages (Viswanadham & Raghavan, 2000). An additional production management choice is made between push and pull strategies (Hopp & Spearman, 2004): a pull production policy explicitly limits the amount of work-in-process that can be in the system, while in a push one no explicit limit on the amount of work-in-process is defined. Wellknown push systems are the Material Requirements Planning (MRP) approach and the Manufacturing Resources Planning technique (also known as MRP II) (Hopp & Spearman, 2004). Well-established pull strategies are the just-in-time technique based on Kanbans and the CONWIP (or CONstant Work-In-Process) strategy (Hopp & Spearman, 2004; Spearman et al., 1990), which is a generalized form of the Kanban policy: while the latter procedure establishes a fixed limit on work-in-process in a part of the system via the limited number of Kanban cards, the CONWIP strategy limits the total number of parts allowed in the whole system at any time, so that the SC is controlled at a constant level of work-in-process. Together with the operational planning and management policy, inventory systems play a very important role in SC management. Inventory management addresses two fundamental issues: when a stock should replenish its inventory (order timing choice) and how much it should order from suppliers for each replenishment (order size choice) (Chen et al., 2005). These choices have to be adequately made in order to protect the SC from uncertainties, such as variations from their nominal values of demand quantity and mix, of production and transportation capacities, of quality and reliability of deliveries etc. (Rota et al., 2002). In particular, the numerous inventory management models proposed in the related literature may be mainly classified in four types, depending on order frequency and quantity. More precisely, order frequency may be either fixed, as in periodic review systems (T), or variable (R); similarly, order quantity may be either fixed, as in continuous review systems (Q), or variable (L). Accordingly, we may distinguish the following categories of inventory management rules (Vollmann et al., 2004): i. (T,Q), in which orders are emitted with given frequency 1/T and ordered quantities are fixed and equal to Q, as in the well known economic order quantity model; ii. (R,Q), where fixed quantities of parts that are Q in number are ordered any time the stock level drops below the reorder point R, as in the reorder point based rules; iii. (T,L), where at every time step T a variable quantity of material is ordered so as to reach the preset desired level L, as in the reorder level rules; iv. (R,L), where variable quantities are ordered to reach the preset level L each time the inventory level drops below the reorder point R.

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Summing up, under the chosen SC management technique, a customer order for a product triggers a series of activities in the SC entities, and these have to be synchronized, so that the consumer demand and the selected inventory control rules are simultaneously satisfied. This chapter focuses on SCs governed either by the MTS policy, which is typical of standardized products with high volumes (and reasonably accurate forecasts), or by the MTO strategy, which is characteristic of customized goods with low volumes. For the sake of brevity the ATO or BTO strategy is not considered in detail, although the presented SC model could be straightforwardly adapted to systems governed by such a strategy. Moreover, the (R,Q) inventory control rule is applied to manage the inventory of buffers that are governed as follows. Any time a withdrawal is made, a control system tracks the remaining inventory level of the buffer of products to determine whether it is time to reorder: in practice, thanks to automation and information systems, these reviews are continuous. At each review, the inventory level is compared with the pre-set reorder point R. In case the inventory level is higher than R, then no change in the inventory occurs. On the contrary, if the inventory level is lower than R, then a fixed quantity Q of products or lots of the considered items is ordered upstream, i.e., Q products or lots are manufactured if the considered stock level refers to an output product, or else they are ordered from an upstream facility in the SC.

3. First order hybrid Petri nets In this section we briefly outline the basics of the FOHPN formalism (Balduzzi et al., 2000). 3.1 The FOHPN structure and marking A FOHPN is a bipartite digraph described by the seven-tuple PN=(P, T, Pre, Post, Δ, F, RS). The set of places P=Pd ∪Pc is partitioned into a set of discrete places Pd (represented by circles) and a set of continuous places (represented by double circles). The set of transitions T=Td ∪Tc is partitioned into a set of discrete transitions Td and a set of continuous transitions Tc (represented by double boxes). Moreover, the set of discrete transitions Td=TI ∪TS ∪TD is further partitioned into a set of immediate transitions TI (represented by bars), a set of stochastic transitions TS (represented by boxes and including exponentially distributed transitions as well as transitions with triangular distribution) and a set of deterministic timed transitions TD (represented by black boxes). We also denote Tt=TS∪TD, indicating the set of timed transitions. The matrices Pre and Post are the pre-incidence and the post-incidence matrices, respectively, of dimension |P|×|T|. Note that the symbol |A| denotes the cardinality of set A. Such matrices specify the net digraph arcs and are defined as follows:

We require that for all t∈Tc and for all p∈Pd it holds Pre(p,t)=Post(p,t) (well-formed nets). The function Δ: Tt→ specifies the timing associated to timed transition. In particular, we associate to each tj∈TS the average firing delay Δ(tj)= δj=1/ λj, where λj is the average firing rate of the transition. More precisely, in case the transition is exponential δj represents the expected value of the associated distribution, while in case it is triangular δj represents the


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modal value of such a distribution and we assume that the minimum and maximum values of the range in which the firing delay varies equal respectively dδj=0.8 δj and Dδj=1.2 δj. In addition, each tj∈TD is associated the constant firing delay Δ(tj)= δj. Moreover, the function F: × specifies the firing speeds associated to continuous transitions (we denote Tc → = ∪{+∞}). For any continuous transition tj∈Tc we let F(tj)=(Vmj,VMj), with Vmj≤VMj, where Vmj represents the minimum firing speed and VMj the maximum firing speed of the generic continuous transition. Finally, the function RS: Td→ associates a probability value called random switch to conflicting discrete transitions. Given a FOHPN and a transition t∈T, the following place sets may be defined: •t={p∈P: Pre(p,t)>0} (pre-set of t); t•={p∈P: Post(p,t)>0} (post-set of t). Moreover, the corresponding restrictions to continuous or discrete places are defined as (d)t=•t∩Pd or (c)t=•t∩Pc. Similar notations may be used for pre-sets and post-sets of places. The incidence matrix of the net is defined as C=Post–Pre. The restriction of C to PX and TY (with X,Y∈{c, d}) is denoted by CXY. A marking

is a function that assigns to each discrete place a non-negative

number of tokens, represented by black dots, and to each continuous place a fluid volume; mi denotes the marking of place pi. The value of a marking at time τ is denoted by m(τ). The restrictions of m to Pd and to Pc are denoted by md and mc, respectively. A FOHPN system 〈 PN,m(τ0) 〉 is a FOHPN with initial marking m(τ0). The following statements rule the firing of continuous and discrete transitions: 1. a discrete transition t∈Td is enabled at m if for all pi∈•t, mi>Pre(pi,t); 2. a continuous transition t∈Tc is enabled at m if for all pi∈(d)t, mi>Pre(pi,t). Moreover, we say that an enabled transition t∈Tc is strongly enabled at m if for all places pi∈(c)t, mi>0; we say that transition t∈Tc is weakly enabled at m if for some pi∈(c)t, mi=0. In addition, for any continuous transition tj∈Tc its IFS is indicated by vj and it holds: 1. if tj is not enabled then vi=0; 2. if ti is strongly enabled, then it may fire with any firing speed vj∈[Vmj,VMj]; 3. if tj is weakly enabled, then it may fire with any firing speed vj∈[Vmj,Vj], where Vj≤VMj depends on the amount of fluid entering the empty input continuous place of ti. We denote by v(τ)=[v1(τ) v2(τ)… v|Tc|(τ)]T the IFS vector at time τ. Hence, any admissible IFS vector v at m is a feasible solution of the following set of linear constraints:

(1)

where Tε(m) ⊂Tc (Tυ(m) ⊂Tc ) is the subset of continuous transitions that are enabled (not enabled) at m and Pε(m) = {pi∈Pc|mi=0} is the subset of empty continuous places. In particular, the first three constraints in (1) follow from the firing rules of continuous transitions, while the last constraint in (1) imposes that if a continuous place is empty then its fluid content does not become negative.

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The set of all feasible solutions of (1) is denoted as S(PN,m). 3.2 The FOHPN dynamics The dynamics of the hybrid net combines both time-driven and event-driven dynamics. We define macro-events the events that occur when: i. a discrete transition fires or the enabling/disabling of a continuous transition takes place; ii. a continuous place becomes empty. The equation that governs the time-driven evolution of the marking of a place pi∈Pc is: (2) Now, if τk and τk+1 are the occurrence times of two subsequent macro-events, we assume that within the time interval [τk,τk+1) (macro-period) the IFS vector v(τk) is constant. Then the continuous behavior of an FOHPN for τ∈[τk,τk+1) is described by: (3) The evolution of the net at the firing of a discrete transition tj∈Td at m(τk-) yields the following marking: (4) where σ(τk) is the firing count vector associated to the firing of transition tj at time τk. Moreover, we associate to each timed transition tj∈Tt a timer νj and we call ν(τk) the vector of timers associated to timed transitions at time τk. Hence, the timer evolution within the macro-period [τk,τk+1) for each transition tj∈Tt is as follows: (5) Whenever tj is disabled or it fires, its timer is reset to zero. Equations (3)-(4)-(5) describe the dynamics of the FOHPN model. The overall state of the system at time τk is given by the marking of all places and by the values of all timers and is hence indicated by

Moreover, the system input is vector

collecting the length of the current

macro-period and the transition (if any) that will fire at the end of such macro-period. Note that this input vector depends on the current state vector x(τk) and on the next macro-event occurring at the end of the current macro-period. Consequently, a FOHPN system (3)-(4)-(5) can be described by a linear discrete-time time-varying state variable model, so that an efficient simulation algorithm determining the state vector at the beginning of each macroperiod, given the initial state x(τk), may be straightforwardly derived.


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3.3 The FOHPN control Once the set of all admissible IFS vectors has been defined, a procedure is required to select one among them and thus let equation (3) of the net dynamics be univocally determined. In other words, each IFS vector v∈S(PN,m) solving (1) represents a particular mode of operation of the system described by the FOHPN. Consequently, the designer may choose the best operative condition according to a given objective and solving the corresponding optimization problem with the constraint set (1). In this chapter the following two cases are considered. 1) Maximize flows. We may consider as optimal the solution v* of (1) that maximizes the performance index J=1T· v, which is intended to maximize the sum of all the flow rates. In the manufacturing domain, this corresponds to maximizing resource utilization. 2) Minimize stored fluid. We may choose as optimal the vector v* solving (1) that minimizes the derivative of the marking of each place p∈Pc. This can be done by minimizing the performance index J=cT· v where cj=C(p,tj) if tj∈p(c)∪(c)p and 0 otherwise. In the manufacturing domain, this corresponds to minimizing the work-in-process. 3.4 An example of FOHPN In this section we describe an example of FOHPN in order to clarify its dynamics.

Fig. 2. An example of FOHPN (a) and its evolution (b). Consider the net in Fig. 2a. Places p1 and p2 are continuous places and places p3 and p4 are discrete places. Transitions t1 and t2 are continuous transitions with firing speeds v1∈[0,V1] and v2∈[0,V2], respectively. We assume V1b>V2a (here a and b are the arc weights in Fig. 2a). In addition, the discrete transitions t3 and t4 are exponentially distributed timed transitions with average firing rates λ3 and λ4, respectively.

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The net dynamics, depicted in Fig. 2b, is described as follows. Since place p4 is initially marked, transition t1 is enabled. Moreover, the initial markings of the continuous places are m1(τ0)>0 and m2(τ0)>0 so that transitions t1 and t2 are both strongly enabled and may fire according to the set of constraints (1):

(6) We assume v1=V1 and v2=V2. By (3), the continuous marking of the net during this first macro-period Δ1 is

for τ>τ0 until the subsequent

macro-event. Moreover, by (5) the timer vector is ν (τ)=[0 τ−τ0]T for τ>τ0, since t3 is disabled and t4 is enabled. Figure 2b shows the corresponding marking evolution and the IFSs of the net continuous transitions. In particular, we remark that the marking m1 increases while m2 decreases since it holds V1b>V2a. At time τ1 a macro-event occurs because place p2 becomes empty. Consequently, t1 becomes weakly enabled and the set of constraints (1) has to be re-written as follows:

(7)

Since t2 remains strongly enabled, its firing speed is assumed v2=V2. On the other hand, we choose the firing speed of t1 as v1=V2(a/b). Therefore, during the macro-period Δ2, by (3) the continuous marking is expressed by

for τ>τ1 until the subsequent

macro-event (see Fig. 2b). Moreover, by (5) it holds ν (τ )=[0 τ −τ1]T for τ>τ1. Next, suppose that at time τ 2 transition t4 fires and the macro-event updates the discrete markings to m3(τ2)=1 and m4(τ2)=0. Hence, t1 is disabled, i.e., v1=0, while t2 remains strongly enabled and we assume v2=V2. Then, during the macro-period Δ3 the marking is given, as in (5), by

(see Fig. 2b). Moreover, by (5) it holds

ν (τ)=[τ −τ2 0]T for τ>τ2.

4. The SC model Based on the idea of the bottom-up approach (Zhou & Venkatesh, 1998), this section reviews a modular FOHPN model to describe a SC previously proposed by the authors (Dotoli et al., 2008). Such a method can be summarized in two steps: decomposition and composition. Decomposition consists in partitioning a system into several subsystems. In SCs this subdivision can be performed based on the determination of distributed system entities (i.e., suppliers, manufacturers, distributors, customers and transporters). All these subsystems are modelled by FOHPNs modules. On the other hand, composition involves the interconnections of these sub-models into a complete model, representing the whole SC.


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In particular, manufacturers are described by continuous transitions, buffers are continuous places and products are represented by continuous flows (fluids) routing from manufacturers, buffers and transporters. Moreover, transporters are described by discrete stochastic transitions with a triangular distribution and the customers demand is modelled by exponential transitions. In addition, exponential transitions model discrete events occurring stochastically in the system, such as the blocking of a raw material supply or of a transport operation due to unpredictable events. Hence, the state of the SC model at the beginning of each macro-period is a vector x(τk) that includes the following sub-vectors: the sub-vector mc(τk), collecting the markings of the continuous places, i.e., the buffer places and the associated capacity places (absent for infinite capacity buffers); the sub-vector md(τk), collecting the markings of the discrete places, i.e., the places modeling choices, constraints and the operative states of entities; the timers vector ν(τk), collecting the values of the timers of discrete timed transitions, i.e., the transitions associated to customer demands or transporters and the transitions modeling the blockings of supplies or transports due to unpredictable or external events. The following FOHPN modules model the individual subsystems composing the SC (Dotoli et al., 2008).

Fig. 3. The FOHPN modeling the input buffers.

Fig. 4. The FOHPN modeling the suppliers.

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4.1 The inventory management model of the input buffers In this section we describe the model of the input buffers of manufacturers and distributors managed by the (R,Q) policy. On the other hand, the output buffers are not managed by the (R,Q) policy since they are devoted just to providing the requested material. The basic quantities of the (R,Q) inventory management strategy are: the fixed order quantity Q; the lead time, i.e., the time between placing an order and receiving the goods in stock; the demand D, i.e., the number of units to be supplied from stock in a given time period; the reorder level R, i.e., the new orders take place whenever the stock level falls to R. Figure 3 shows the FOHPN model for the input buffers managed by the (R,Q) policy (Furcas et al., 2001). The continuous place pB denotes the input buffer of finite capacity CB. The complementary place p’B models the available buffer space so that at each time instant it holds mB+m’B=CB. Here and in the following models the assumed initial marking corresponds to empty buffers. We assume that the buffer can receive demands from different facilities and can require the goods from different transporters. Hence, each demand is modelled by a continuous transition tDi with i=1,…,m so that the demand to be fulfilled is Di=vDiQ’i. When mB>0 a transition tDi with i∈{1,…,m} may fire at the firing speed vDi, reducing the marking of the place pB with a constant slope vDiQ’i. As soon as mB falls below the level RB (or, equivalently, the marking m’B goes over CB–RB), the immediate transition t1 is enabled. When t1 fires, the choice place pC∈Pd becomes marked and performs the choice of the input facility. Hence, new materials/products are requested by enabling one of the transitions tTi according to the value of the random switches RS(tTi) with i=1,…,n. If a particular transition tTi with i∈{1,…,n} is selected and fires after the lead time of averageΔ(tTi)= δi=1/ λi, Qi products are received in the buffer and CB–RB–Qi units are restored in the buffer capacity. Typically, transitions tTi can represent a transport operation. 4.2 The inventory management model of the SC entities The supplier model. Suppliers are modelled as a continuous transition and two continuous places (see Fig. 4). The continuous place pB represents the raw material output buffer of finite capacity CB and the complementary place p’B represents the available buffer space. Moreover, the continuous transition t1 models the arrival of raw material into the system. In addition, we consider the possibility that the providing of raw material is blocked. This situation is represented by a discrete event modelled by two exponentially distributed transitions and two discrete places. In particular, place pk ∈ Pd models the operative state of the supplier and p’k ∈ Pd is the non-operative state. The blocking and the restoration of the raw material supply correspond to the firing of exponential transitions tk and t’k, respectively. For the sake of clarity, Fig. 4 depicts the transition tT ∈ TS that, as discussed later, models the transport operation. Here and in the following models the initial marking assumes that the entity is operative. The manufacturer and assembler model. Manufacturers and assemblers are modelled by the FOHPN shown by Fig. 5. More precisely, the continuous places pBi and p’Bi with i=2,…n describe the input buffers and the corresponding available capacities, respectively. Each buffer stores the input goods of a particular type. Analogously, the continuous places pB1 and p’B1 model the output buffer and its capacity, respectively. The production rate of the facility is modelled by the continuous transition t1 with the assigned firing speed v1∈[Vm1,V1]. The transporter model. The transporters connecting the different facilities are modelled by a set of timed transitions tTi with triangular distributions for i=1,…,n (see Fig. 6), according to


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(Kelton et al., 2004; Law & Kelton, 2000). Each transition describes the transport of items of a particular type from an upstream facility to a downstream one in an average time interval Δ(tTi)= δi=1/ λi. The control places pCi∈Pd with i=1,…,n determine the choice of only one type of material to transport among the available set by the firing of the corresponding immediate transition ti with i=1,…,n modeling the replenishment request to the transporter by a downstream SC entity. In addition, place p1∈Pd disables the remaining transitions. Moreover, the random stop of the material transport is represented by two places pk, p’k∈Pd and two exponentially distributed transitions tk,t’k∈TS. The transporter capacity is Q and places pBi∈Pb and p’Bi∈P’b with i=1,…n in Fig. 4 describe the n input buffers of the downstream facility (e.g., a manufacturer, a distributor, a retailer) and the corresponding available capacities, respectively. The initial marking shown assumes that no material has yet been selected for transportation.

Fig. 5. The FOHPN modeling manufacturers and assemblers.

Fig. 6. The FOHPN modeling the transporters.

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Fig. 7. The FOHPN modeling the distributors.

Fig. 8. The FOHPN modeling the retailers under the MTS strategy. The distributor model. The model of the distributors is represented by an input buffer managed by the (R,Q) inventory control rule. Hence, the model is similar to the FOHPN represented in Fig. 3, where each downstream continuous transition tDi with i=1,…,m is substituted by a stochastic timed transition representing a transport operation (see Fig. 7). The retailer model. The retailer model is different in the two cases of the MTS and MTO management. If the MTS strategy is used, the retailer is represented by an input buffer managed by the (R,Q) policy with a finite lead time and stochastic demand (see Fig. 8). Hence, the model is similar to the FOHPN represented in Fig. 3 where all the downstream continuous transitions are substituted by one or more exponential transition (such as t1) modeling the stochastic demand of the consumers. Moreover, the continuous place pF


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collects all the products obtained by the retailer, i.e., it corresponds to an infinite capacity buffer. In addition, the discrete transition with triangular distribution tL models the deterioration of the finished products used by the customer that are stored in the infinite capacity buffers pS and pD. In particular, pS collects the μQ2 products to be demanufactured with μ∈[0,1], and pD the (1-μ)Q2 goods to be discarded. In addition, transition tT represents the transport operation transferring products to the demanufacturer. Similarly, under the MTO management the retailer (see Fig. 9) is described by an output buffer place pB∈Pc and a stochastic transition t1 modeling the rate with which the consumer withdraws products, that in this case have been previously ordered. The infinite capacity place pF ∈ Pc collects all the products obtained by the retailer. In addition, transition tL models the products deterioration, place pS denotes the system output and transition tT represents the transport operation. The customer demand, just as under the MTS policy, is represented by one (or more) exponential transition, such as t2. On the other hand, differently than the MTS case, when fired transition t2 stores an order of Q products in the infinite capacity buffer place pO. Hence, its marking mo enables transition t3 modeling the production of the upstream manufacturer. When the Q orders are satisfied, marking mo becomes zero, t3 is weakly enabled and its firing speed is equal to zero until the upstream transition t2 fires again. The de-manufacturer model. De-manufacturers are modelled by the FOHPN shown by Fig. 10, which is the reverse of the manufacturers model reported in Fig. 5. More precisely, pB1 and p’B1 model the input buffer and its capacity, respectively, and the continuous places pBi and p’Bi with i=2,…n describe the output buffers and the corresponding available capacities, respectively. The continuous transition t1 models the production (or, more properly, the disassembly) rate of the facility.

Fig. 9. The FOHPN modeling the retailers under the MTO strategy.

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Fig. 10. The FOHPN modeling the de-manufacturers.

5. A case study We consider the SC of Fig. 1 described in Section 2.B. To implement and compare the system managed under the MTS and MTO strategies, we model the whole SC properly merging the elementary modules described in the previous section. Figures 11 and 12 show the FOHPN modeling the SC under MTS and MTO, respectively. The dashed rectangles depict in the two figures the correspondence between each module and the entities of Fig. 1. We remark that for the sake of simplicity in this chapter we consider only single product SCs, since our ultimate aim is to show the effectiveness of a FOHPN formalism for operational management of SCs. However, common multi-product SCs may straightforwardly be considered within the proposed formalism thanks to its simplicity and modularity. 5.1 The SC under the MTS strategy Figure 11 shows the SC model of the case study managed by the MTS policy. The production is determined by the firing of the continuous transitions t1, …, t7 (modules S1, S2 and S3) that describe the input of the raw materials that can be interrupted by stochastic events only. Consequently, under this control technique, each input buffer is managed by the (R,Q) strategy. Moreover, if the input buffer of manufacturer M1 (M2) requires a particular product, a request has to be sent to the corresponding transporter. Hence, places p60, p63, p66 and p67 (modules T1 and T2) (p72, p74, p75 and p77 (modules T3 and T4)) are introduced to select a particular transporter. For example, if place p60 (module T1) is marked then the transport modelled by t43 (module T1) is enabled. In addition, transitions t56 and t58 and place p63 (modules T1 and T2) are introduced since the buffer of M1 storing the semi-finished products monitors (denoted by p17 and p’17) can require material from S1 by T1 or from S2 by T2. Consequently, place p63 with transitions t56 and t58 model the choice. Finally, according to the SC scheme of Fig. 1, in the model of Fig 11 the supply of some semifinished products at the manufacturers (i.e., H at place p21 in M1 and C at place p27 in M2) may be obtained via two different paths, either by a supplier or by the demanufacturer. The corresponding choice of the replenishment transition to enable (i.e., t60 of T2 or t82 of T11 for M1 and t63 of T4 or t83 of T12 for M2) is modelled via a random switch that associates a 100% probability to the less costly supply obtained by the de-manufacturer (i.e., to the enabling of t82 and t83): the corresponding transition, however, is enabled via the


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respective arc weights Q13 and Q14 only when the corresponding semi-finished product output buffer of the demanufacturer (i.e., place p96 or p97 of module DM1) contains sufficient material.

Fig. 11. The FOHPN modeling the case study under the MTS policy.

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Fig. 12. The FOHPN modeling the case study under the MTO policy.


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5.2 The SC under the MTO strategy Figure 12 shows the SC model of the case study managed by the MTO policy. The model is similar to the model shown in Fig. 11 with two exceptions. First, some additional edges, places and transitions are introduced and drawn in bold. Second, the retailer modules are different than those employed under the MTS strategy, as detailed in Section 5.B. Hence, the actual assembling of a finished product is triggered by the demand of a consumer that in the MTO policy is modelled by the discrete exponential transitions t40 and t41 (modules R1 and R2, respectively) and by the places p104 and p105 (modules R1 and R2, respectively). For example, if a request is present for R1 (i.e., m104>0), then t8 (module M1) is enabled and fires. Consequently, the markings of p15, p17, p19 and p21 (module M1) decrease. In such a condition, if there is material stored in each input buffers, no raw material is requested to the suppliers. Indeed, the markings m106=m109=m64=m66=m67=0 (modules T1 and T2) disable transitions t1, t2, …, t5 (modules S1 and S2). On the contrary, if for example the input buffer p19 (module M1) is at a low level (i.e., it holds m19≤R3 and m’19≥C19-R3), then transition t59 (module T2) is enabled. If such an immediate transition fires, it holds m66=1 and the continuous transition t4 (module S2) fires to provide the raw material C in the buffer p7 (module S2). When it holds m7≥Q2, transition t45 (module T2) modeling the transport is enabled and fires after Δ(t45) time units on average. After transition t45 fires, it holds m66=0 so that t4 is disabled. We remark that the replenishment requests of a manufacturer input buffer might influence the replenishment of other manufacturer input buffers. Hence, in order to avoid such a condition, we suitably introduce places p106, p107, p108, p109, p110 and p111 and transitions t86, t87, t88 and t89 (modules T1, T2 and T3, drawn in bold). As an example, a token in p106 (module T1) may be determined by the firing of transition t86 (module T1) or t87 (module T3). In both cases, the marking m106=1 enables transition t2 (module S1). The subsequent enabling of the transport transition is managed by the added places p107 (module T1) and p108 (module T3) that respectively enable the transports from supplier S1 to manufacturers M1 and M2 modelled by t43 (module T1) and t48 (module T3). Finally, note that the supply of the semi-finished products at the manufacturers from suppliers or de-manufacturers is governed in the same way as in the previously detailed SC model managed by MTS. 5.3 The simulation specification The SC dynamics under the MTS and MTO management strategies is analyzed via numerical simulation using the data reported in Table 1. This table shows the manufacturer production rates and the average firing delays of discrete stochastic transitions. In addition, Table 2 shows further data necessary to fully describe and simulate the system, namely the buffer capacities for the inventories of each stage and the initial markings of odd continuous places (those of the other continuous places are complementary with respect to the capacities reported in the table and hence omitted). Furthermore, the initial markings of discrete places and the values of the edge weights are depicted in Figs. 11 and 12. Moreover, Table 3 reports the reorder levels and fixed order quantities. Note that the reorder levels are set to zero when the MTO policy is used, since the production is triggered by the customers demand only. In addition, we assign a value of 0.5 to each random switch. Moreover, the fraction of consumed goods to be recycled is set equal to μ=0.5 in both retailers (see modules R1 and R2 of Figs. 11 and 12).

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Table 1. Firing speed and average firing delay of continuous and discrete transitions.

Table 2. Initial marking of odd continuous places, capacities and edge weights. To analyze the SC, the following performance indices are selected (Viswanadham, 1999): i. the system throughput T, i.e., the average number of products obtained in a time unit; ii. the average system inventory SI, i.e., the average amount of products stored in all the system buffers during the run time TP; iii. the average lead time LT=SI/T that is a measure of the time spent by the SC to convert the raw material in final products. Note that in the considered simulation experiments the SI performance index (and, consequently, the LT value) is calculated taking into account only the buffers that are upstream with respect to the retailers. The FOHPN models of the case study under the MTS and MTO policies are implemented and simulated in the Matlab environment (The Mathworks 2006), which is ideally suited when dealing with modular, numerical matrix-based large-scale systems. All the indices are evaluated by a simulation run of 600 time units with a transient period of 100 time units, so that the run time equals 500 hours if we associate one time unit to one hour. The estimates of


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the performance indices are deduced by 100 independent replications with a 95% confidence interval. Besides, we evaluate the percentage value of the confidence interval half width to assess the accuracy of the performance index estimation: the half width of the confidence interval, being 3.3% in the worst case, confirms the sufficient accuracy of the performance indices estimation.

Table 3. Reorder levels and fixed order quantities. The simulation study is performed in three different operative conditions, respectively denoted OCi with i=1,2,3, and each operative condition OCi corresponds to a different choice of the IFS vector within the set of admissible solutions of (1). Operative Condition 1 (OC1). At each macro-period the IFS vector v is selected so as to maximize the sum of all flow rates: (8) Operative Condition 2 (OC2). At each macro-period the IFS vector v is selected so as to minimize the sum of all the stored materials: (9) with cj=C(p,tj) if tj∈p(c)∪(c)p and cj=0 otherwise. Operative Condition 3 (OC3). At each macro-period the IFS vector v is selected so as to maximize the sum of all flow rates as in (8) while setting all the capacities, re-order levels and fixed order quantities of the SC equal to 3/5 of the nominal values reported in Tables 2 and 3. We remark that the first operative condition allows us to estimate the maximum level of performance of the SC with respect to the production capacity. Obviously, if we wish to maximize the manufacturing of a sub-set of products only, then (8) may be accordingly modified with a suitable objective function. Similarly, in the second operative condition the

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stored materials in the SC buffers are minimized and, if we wish to minimize the stocks of inventory of a sub-set of products, then (9) may be accordingly modified by a suitable vector cT. Moreover, the third operative condition aims at imposing an almost constant workinprocess in each buffer as a fraction of each capacity, similar to the previously described CONWIP management technique (Spearman et al. 1990). In this case the storage costs are a priori limited and at the same time the SC does not evolve at its maximum productivity level. 5.4 The simulation results Figures 13, 14 and 15 report the selected SC performance indices, i.e., throughput, system inventory and lead time, respectively, obtained under the two management strategies and in the three operative conditions. In particular, Fig. 13 shows that in the two conditions OC1 and OC2 the system throughput values obtained under the MTS strategy are always greater than the corresponding values obtained with the MTO policy, since in the latter case the production is triggered by orders only. On the other hand, the reduced productivity imposed to the SC under OC3 leads to equivalent throughput values in such a condition under MTS and MTO. Moreover, the throughput values obtained under OC1 for a given management strategy (i.e., MTS or MTO) are greater than the ones corresponding to OC2 and these are in turn bigger than those obtained in OC3. Indeed, solving (8) corresponds to maximizing the flow rates of the SC while the objective of (9) is to minimize the SC inventory and finally the third condition corresponds to a decreased level of the SC overall maximum productivity. In addition, it is noteworthy that the greatest (smallest) average throughput value, i.e. the highest (lowest) productivity level, is obtained when the SC is governed by the MTS (MTS or MTO) policy and in OC1 (OC3).

Fig. 13. Throughput (parts per hour) for different operative conditions under MTS and MTO.


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Fig. 14. System inventory (parts) for different operative conditions under MTS and MTO.

Fig. 15. Lead time (hours) for different operative conditions under MTS and MTO. On the other hand, Fig. 14 shows that in the three cases the system inventory values evaluated under the MTS policy are always much greater than the corresponding values obtained with the MTO strategy. Moreover, the values of index SI obtained in OC1 under a given policy (i.e. MTS or MTO) are greater than the ones corresponding to OC2 for the same

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strategy and these are in turn bigger than those obtained in OC3 with that policy. The greatest (smallest) average system inventory value, i.e. the highest (lowest) storage level, is obtained when the SC is governed by the MTS (MTO) policy and in OC1 (OC3). Besides, Fig. 15 shows that in any operative condition the lead time values obtained under MTS are always greater than those obtained with the MTO policy, since in the former case the higher production corresponds on average to a longer permanence of materials and products in the system. Furthermore, Fig. 15 shows that under a the MTS operational management policy the LT values obtained in OC1 are greater than the corresponding ones obtained in OC2, since the former case corresponds to a higher productivity. On the other hand, in OC3 the value of LT under MTS (MTO) is greater than the corresponding index obtained in OC1 under the same policy, since in the former condition the considerable throughput diminishment counterbalances the less significant system inventory decrease. Conversely, the MTO strategy lead to similar LT values in the three operative conditions. Summing up, the SC managed under MTS is more productive than the system using MTO. On the other hand, using the latter policy the stocks in the considered SC are reduced and the same applies to the lead time values. Moreover, a different choice of the production rates and inventory management (as in OC1, OC2 and OC3) lets the designer further find a tradeoff between different key performance indicators of the SC. For instance, comparing the system under the MTS policy in OC1 and OC2, it is apparent that in these conditions a similar productivity level is attained (compare the corresponding throughput values in Fig. 13) but in the latter case the system inventory is quite smaller (as enlightened by the SI values in Fig. 14), leading to more sustainable storage costs than in the former case, with a lower lead time, as well (see the LT values in Fig. 15). Moreover, OC3 under the MTS or MTO policy is characterized by the lowest value of average throughput and highest value of lead time compared to the other operative conditions with the same management policy (see Fig. 13 and Fig. 15) while offering the lowest average system inventory (see Fig. 14). On the other hand, under a given management strategy OC1 is characterized by a lower value of LT with respect to OC3 (see Fig. 15) but OC1 involves a higher SI value than OC3, while leading to a better (or at most equal) throughput (see Fig. 13 and Fig. 14). With regard to the MTO strategy, the obtained LT values in the three operative conditions are quite similar (see Fig. 15), with OC3 leading to the lowest SI index (see Fig. 14) at the price of a low value of T (see Fig. 13).

6. Conclusion The chapter focuses on the problem of managing at the operational level Supply Chains (SCs), new emerging company networks, very complex to describe and manage. The SC system is described by a modular model based on the first order hybrid Petri net formalism, previously proposed by the authors: a fluid approximation of material and products is considered and discrete unpredictable events occurring stochastically (i.e., blocking of suppliers, manufacturers, transporters, etc.) are singled out by the discrete event dynamics. The model can effectively describe the operational management policies and the inventory control rules, and enables the designer to impose an optimal SC dynamics according to given objective functions. To show the effectiveness and simplicity of the modeling technique, a SC case study is modelled and simulated under the alternative Make-To-Stock and Make-To-Order policies with a reorder point based inventory rule. The simulation results show that the fluid


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approximation leads to an effective management policy implementation and to the possibility of choosing some important control parameters of the system, such as the production rates. Perspectives on future research include investigating the optimal decoupling point position, as well as implementing additional inventory control rules.

7. References Alla, H. & David, R. (1998). Continuous and hybrid Petri nets. Journal of Circuits, Systems and Computers, Vol. 8, No. 1, 159-188, ISSN 0218-1266. Balduzzi, F.; Giua, A. & Menga, G. (2000). Modelling and control with first order hybrid Petri nets. IEEE Transactions on Robotics and Automation, Vol. 4, No. 16, 382-399, ISSN 1042-296X. Chen, H.; Amodeo, L.; Chu, F. & Labadi, K. (2005). Modeling and performance evaluation of supply chains using batch deterministic and stochastic Petri nets. IEEE Transactions on Automation Science and Engineering, Vol. 2, No. 2, 132-144, ISSN 1545-5955. Chopra, S. & Meindl, P. (2001). Supply chain management: strategy, planning, operation. Prentice Hall, ISBN 0130264652, Upper Saddle River. Desrochers, A.; Deal, T.J. & Fanti, M.P. (2005). Complex-valued token Petri nets. IEEE Transactions on Automation Science and Engineering, Vol. 2, No. 4, 309-318, ISSN 15455955. Dotoli, M. & Fanti, M.P. (2005). A generalized stochastic Petri net model for distributed manufacturing system management. Proceedings of CDC-ECC’05, 44th IEEE Conference on Decision and Control and European Control Conference, pp. 2125-2130, ISBN 0-7803-9568-9, Seville, Spain, December 12-15, 2005, IEEE, Seville, Spain. Dotoli, M.; Fanti, M.P.; Iacobellis, G. & Mangini, A.M. (2008). A first order hybrid Petri net model for supply chain management, to appear on the IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955. Dotoli, M.; Fanti, M.P.; Meloni, C. & Zhou, M.C. (2005). A multi-level approach for network design of integrated supply chains. International Journal of Production Research, Vol. 43, No. 20, 4267-4287, ISSN 0020-7543. Dotoli, M.; Fanti, M.P.; Meloni, C. & Zhou, M.C. (2006). Design and optimization of integrated e-supply chain for agile and environmentally conscious manufacturing. IEEE Transactions on Systems Man and Cybernetics, part A, Vol. 36, No. 1, 62-75, ISSN 1083-4427. Elmahi, I.; Grunder, O. & Elmoudni, A. (2003). A max plus algebra approach for modelling and control a supply chain. Proceedings of IEEE-CCA’2003, 2003 IEEE International Conference on Control Applications, pp. 1425-1430, ISBN 0-7803-7729-X , Istanbul, Turkey, June 23-25, 2003, IEEE, Istanbul. Furcas, R.; Giua, A.; Piccaluga, A. & Seatzu, C. (2001). Hybrid Petri net modelling of inventory management systems, European Journal of Automation APII-JESA, Vol. 35, No. 4, 417-434, ISSN 0296-1598. Gaonkar, R.S. & Viswanadham, N. (2001). Collaboration and information sharing in global contract manufacturing networks. IEEE/ASME Transactions on Mechatronics, Vol. 6, No. 4, 366-376, ISSN 10834435. Hopp, W.J. & Spearman, M.L. (2004). To pull or not to pull: what is the question? Manufacturing and Service Operations Management, Vol. 6, No. 2, 133-148, ISSN 15234614.

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Kelton W.D.; Sadowski R.P. & Sturrock D.T. (2004), Simulation with Arena, Third edition, McGraw-Hill, ISBN 9780072919813, Boston, MA. Law A.M. & Kelton W.D. (2000). Simulation modeling and analysis, Third edition. McGraw-Hill, ISBN 978-0070366985, Boston, MA. Luo, Y.; Zhou, M.C. & Caudill, R.J. (2001). An integrated e-supply chain model for agile and environmentally conscious manufacturing. IEEE/ASME Transactions on Mechatronics, Vol. 6, No. 4, 377-386, ISSN 10834435. Rota, K.; Thierry, C. & Bel, G. (2002). Supply chain management: a supplier perspective. Production Planning and Control, Vol. 13, No. 4, 370-380, ISSN 0953-7287. Shapiro, J.F. (2001). Modeling the supply chain. Duxbury Press, ISBN 978-0-534-37741-0, Pacific Grove. Silva, M., Recalde, L. (2004). On the fluidification of Petri nets: from discrete to hybrid and continuous models, Annual Reviews in Control, Vol. 28, 253-266, ISSN 1367-5788. Spearman, M.L.; Woodruff, D.L. & Hopp, W.J. 1990. CONWIP: a pull alternative to kanban. International Journal of Production Research, Vol. 28, No. 5, 879-894, ISSN 0020-7543. The Mathworks (2006). MATLAB Release Notes for Release 14. The Mathworks, Natick, MA, USA. Vidal, C. & Goetschalckx, M. (1997). Strategic production-distribution models: a critical review with emphasis on global supply chain models. European Journal of Operational Research, Vol. 98, No. 1, 1-18, ISSN 0377-2217. Viswanadham, N. (1999). Analysis of manufacturing enterprises: an approach to leveraging value Delivery processes for competitive advantage. Kluwer Academic, ISBN 185233-186-0, Boston. Viswanadham, N. & Gaonkar, R.S. (2003). Partner selection and synchronized planning in dynamic manufacturing networks. IEEE Transactions on Robotics and Automation, Vol. 19, No. 1, 117-130, ISSN 1042-296X. Viswanadham, N. & Raghavan, S. (2000). Performance analysis and design of supply chain: a Petri net approach. Journal of the Operational Research Society, Vol. 51, No. 10, 11581169, ISSN 0160-5682. Vollmann, T.E.; Berry, W.L.; Whybark, D.C. & Jacobs, F.R. (2004). Manufacturing planning and control systems for supply chain management. Irwin/Mc Graw-Hill, ISBN 9780072299908, New York. Von Mevius, M. & Pibernik, R. (2004). Process management in supply chains - a new Petrinet based approach. Proceedings of the 37th Annual Hawaii International Conference on System Sciences, pp. 71-80, Big Island, Hawaii, USA, January 5-8 2004, IEEE, Big Island, Hawaii, USA. Wortmann, J.C. (1983). A classification scheme for master production schedule. In: Efficiency of manufacturing systems, Berg, C., French, D. Wilson, B., eds, Plenum Press, ISBN 978-0306412561, New York. Wu, T. & O’Grady, P. (2005). A network-based approach to integrated supply chain design. Production Planning and Control, Vol. 16, No. 5, 444-453, ISSN 0953-7287. Zhou, M.C. & Venkatesh, K. (1998). Modeling, simulation and control of flexible manufacturing systems. A Petri net approach. World Scientific, ISBN 981-02-3029X, Singapore.

9 A Physics Approach to Supply Chain Oscillations and Their Control Ken Dozier and David Chang

Western Region Application Center University of Southern California U.S.A.

1. Introduction Virtually all manufacturing involves supplies chains, in which value is added by each entity in the chain until a finished product emerges at the end. A serious problem that plagues all supply chains is unwanted fluctuation in the inventories along the chain. This disrupts the product output, is costly in capital, and can result in considerable disruption and hardship in personal lives. J.D. Sterman and his colleagues at MIT (Sterman & Fiddaman, 1993) have performed a useful service by providing business schools with a widely used simulation game that demonstrates for a beer distribution supply chain how easily the fluctuations can arise, and how difficult they can be to control. Their simulation results indicate that the oscillations are due to the over-reaction to input fluctuations by the individual entities in the chain, and the results strongly suggest that the situation could be improved by information technology that enables real-time feedback. The purpose of this chapter is to describe a statistical physics approach to understanding the supply chain oscillations. Standard techniques from statistical physics can lead to insights on the nature of the oscillations and on means for their control. Much of the supply chain work has been reported earlier, primarily at a number of conferences (Dozier a& Chang, 2004a, 2004b, 2005, 2006a, 2006b, 2007), but this chapter is the first time that the general approach together with its applications have been assembled in one place, along with a number of possible extensions. Section 2 provides some background on the application of statistical physics techniques to manufacturing issues. The focus in this section is on a pseudo-thermodynamics description of a manufacturing sector, and on the nature of effective intervention forces that can be uniquely derived from this thermodynamics. Section 3 demonstrates how the statistical physics approach leads directly to the existence of supply chain normal mode oscillations. It is shown that the nature of the normal modes depends on the extent of the information exchange between the entities in the supply chain. When the information exchange occurs only between an entity and the two entities immediately below and above it in the chain, the normal mode oscillation frequencies depend strongly on the way the oscillation amplitudes change along the chain: the normal modes resemble sound waves. On the other hand, when the information exchange occurs between an entity and all the other entities in the chain, the normal mode oscillation

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frequencies depend more weakly on the way the oscillation amplitudes change along the chain: the normal modes resemble plasma oscillations. Section 4 discusses possible interventions that can increase the production rate of a supply chain, utilizing the effective pseudo-thermodynamic forces discussed in Section 2. It demonstrates that both the time scale and the position focus of the intervention are important for determining the effectiveness of the intervention. An example is given of a useful application to supply chains of a quasilinear approximation technique that is often used in plasma physics problems. Section 5 summarizes the approach and results to date, and suggests several possible extensions.

2. Background on statistical physics modeling in manufacturing There has been a long-term association between economics and one particular aspect of statistical physics: thermodynamics. This association can be found in both neoclassical economics and modern new growth economics. For example, Krugman (1995) points out that economics is based on physics, and one of his favorite examples is that of the thermodynamics of economics. Even systems far from economic equilibrium can be treated by (open system) thermodynamics (Thorne & London, 2000). Costanza, et. al. (1997) have noted that biology has also played an important role, emphasizing that both thermodynamics and biology drove the development of new growth economic models: Smith and Foley (2002) have remarked that both neoclassical economics and classical thermodynamics seek to describe natural systems in terms of solutions to constrained optimization problems. The foregoing observations about the role of thermodynamics in economics are in the quasistatic realm: the variables involved are relatively slowly changing in time. However, statistical physics also treats dynamic (time-dependent) phenomena, and can be used to understand important. time-dependent processes in economics. Koehler (2001, 2003) has been studying the interactions between the private and public sectors, and has emphasized the need to understand how best to conduct interactions between sectors that can have quite drastically different intrinsic time scales. 2.1 Rationale for application of statistical physics Why does statistical physics provide a good framework for analyzing static (timeindependent) and dynamic (time-dependent) aspects of cybernetic-based management of manufacturing? There are two reasons: 1. For the time-independent aspects, the formalism is designed to display the implications of systematically focusing on a few macroscopic variables of interest, an advantage for understanding complex systems such as manufacturing. 2. For time-dependent aspects, the formalism naturally takes into account the impact of time-dependent collective (cooperative) effects on the systems, of importance for designing effective cybernetic management control practices for complex situations. These two reasons are considered further below. 2.1(i). Time-independent aspects: systematic focus on macroscopic variables The systematic focusing on a few macroscopic variables of interest is important for understanding the relationships that can exist between the variables, and the associated implications for how the associated microscopic variables are distributed in an industrial

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sector. For example, suppose the objectives of policies and management actions on a particular industrial sector are to increase the productivity and the sales for the sector. Statistical physics can explicitly exhibit the relationships between the two variables and show how the impact of policies and management on the two are reflected by changes in the distribution of productivities and sales of the companies in the sector. The systematic focusing on a few macroscopic variables of interest is accomplished by the constrained optimization approach of statistical physics. The approach is “constrained” by the requirement that specific measured values can be assigned to the few chosen relevant variables, and it is “optimized” by deriving the probability distribution that corresponds to the maximum possible number of possible arrangements of the entities comprising the system. This approach leads to well-defined “management forces” that can change the distribution of the variables within an industrial sector. 2.1(ii). Time-dependent aspects: collective effects in a sector An understanding of the collective effects in an industrial sector is important for optimizing the focus and timing of both policy and management efforts. In particular, it is well known that companies in supply chains exhibit inventory oscillations that can be larger than the oscillations in market demands. This is wasteful of resources, and impacts costs and profits in a negative manner. Properly focused policy and cybernetic-based management can reduce these oscillations, and statistical physics can provide a systematic guide on how best to focus and time these efforts to perform this cost-saving reduction. 2.2 Effective pseudo-thermodynamic forces In this chapter we would like to consider the impact of interventional control on reducing the resource-wasteful inventory oscillations in supply chains. This involves the application of effective pseudo-thermodynamic forces to the entities in the supply chains. These forces can be most easily understood by considering a thermodynamic description of the quasistatic state of a manufacturing sector. In this subsection, the thermodynamics approach – with its definition of an effective force - is briefly summarized, and some validating examples are given of its successful application to real data. 2.2(i). Conservation laws for information transfer involving effective “information force” Thermodynamics can be applied to manufacturing in a number of ways. As an illustration of a general approach, let us suppose that a particular industrial sector has an objective of decreasing the average unit cost of production, and wishes to do this by implementing information technology transfer from an external source. (For example, this external source could be the government to which the companies in the sector have provided real-time data.) The statistical physics approach will produce a concrete measurable value-added for that sector due to the information transfer. This objective can be posed as a straightforward constrained optimization problem in statistical physics, and the problem can be solved by maximizing a probability distribution subject to taking into account the constraints by the use of Lagrange multipliers. As an example, suppose that the total costs incurred in some length of time, C(total) = ∑ C(i)n(i), is known and that the total number of units produced in that time, N(total) = ∑ n(i), is known, where in both expressions the summation is from i = 0 to i = NT, the total number of companies in the sector. With this knowledge, we then ask what the most likely distribution

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of production is over i Since the total number of allowable ways that N(total) can be arranged subject to an assumed distribution n(i) is P[N(total), n(i)] = N(total)! / [ n(1)!n(2)! … n(NT!], the most likely distribution n(i)can be determined by maximizing ln P subject to the constraints that N(total) and C(total) are known. Introducing the Lagrange multipliers α and β to form F(n(i)) = ln {P[N(total), n(i)]}- α [ ∑ n(i) - N(total) ] - β[∑ C(i)n(i) – C(total)], we find on setting dF(n(i)) /dn(i) = 0 and using the Stirling large number approximation for a factorial,- ln{n(i)}-1 - α - β C(i) = 0, i.e. n(i) = A exp[-βC(i)], the familiar Maxwell-Boltzmann distribution. (A more detailed derivation of the relevant equations is contained in Dozier and Chang (2004).) Given the Maxwell-Boltzmann distribution function, it is straightforward to define quantities that are analogous to the conventional thermodynamic quantities of thermodynamics. These are shown in Table 1:

Table 1. Comparison of statistical formalism in physics and in economics (based on Dozier and Chang (2004a) In this Table, the statistical physics formalism has naturally replaced the product of Boltzmann’s constant kB and the temperature T in the physics realm by the inverse of a “bureaucratic factor β” in the economic realm. Note that the constrained optimization approach leads directly to a generalized pressurelike force corresponding to information technology transfer. (A second example is also provided in Table 1 of an effective force due to the introduction of new manufacturing technology.) For the purposes of this chapter, the important result is that an effective “force” can be derived from a partition function or associated free energy provided by a constrained optimization pseudo-thermodynamics of manufacturing. As a further illustration of the result of applying a statistical physics technique to model a manufacturing sector, a conservation equation relating pseudo-thermodynamic quantities can be obtained by differentiating the expression for the total cost of production. This is shown in Table 2. We note that the conservation equation (that corresponds to the First Law of Thermodynamics) involves the effective force fξ that is uniquely determined by the “free energy function” F. It is also interesting to note that if the two terms in the conservation equation have opposite signs, then the effect of information technology transfer is not as large as it could be. On the other hand, if the two terms have the same sign, then the impact of information technology transfer on costs for the sector are maximized.


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Table 2. Conservation law for information technology transfer (based on Dozier and Chang (2004a) As a preliminary data comparison, these results were applied to two particular sectors (the semiconductor sector and the heavy springs sector) in the U.S. Economic Census Data for the Los Angeles Metropolitan Statistical Area for 1992 and 1997 (Dozier & Chang, 2006b) The per capita information technology investments were known for both of these sectors: they were high for the semiconductor sector and low for the heavy springs sector. The semiconductor sector experienced both a decrease in the average unit cost of production) and a decrease in the entropy term (a lower effective temperature). In this case, both terms in the conservation equation were operating synergistically. The sector also displayed a marked increase in output. The heavy spring sector (the information-poor sector), on the other hand, displayed little change in output. Its entropy term increased and its average unit cost of production also increased. In this case, both terms acted in the wrong direction. Although this preliminary examination of data is not directly applicable to information transfer in general, and although it is not possible to attribute all the changes to information technology investments since several other factors were operating, the examples suggest that both the force and entropy terms (due to all the relevant factors) in the conservation equation can act synergistically in the desired direction. Accordingly, this suggests that information technology transfer can act to both decrease the effective temperature of a sector and to reduce costs. The former might be achieved by focusing information technology transfer efforts on companies that have unit costs that are far out of line with the average unit costs. It might also be achieved by focusing information technology transfer efforts on companies that have the best unit costs, letting the other companies drop by the wayside. Another possible method of decreasing entropy might be to provide market incentives for “heat flow” from the sector to sectors (including the government itself) that have lower temperatures.

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2.2(ii). Static phenomena: Impact of information on productivity in the Los Angeles region As a second example of the application of the constrained optimization technique of statistical physics, the impact of information technology on the improvement of productivity (i.e. on improving the output per employee) was considered. A detailed account of this example is contained in Dozier and Chang (2006b) and is described in the proceedings of the CITSA 04 conference (Dozier & Chang 2004a). The constrained optimization formalism is applied here also, except that now the focus is not on the unit cost of production but rather on the shipments per company. In this example, the technique is used to derive the most likely distribution of the number of companies having a particular value of shipments per company vs. the shipments per company.

Fig. 1. Comparison of U.S. economic census cumulative number of companies vs. shipments/company (diamond points) in LACMSA in 1992 and the statistical physics cumulative distribution curve (square points) with β = 0.167 per $106 The figure above (taken from Dozier and Chang, 2006b) compares the predicted cumulative distribution for a value of “inverse temperature” β = 0.167 per million dollars with actual data for the manufacturing sectors in the Los Angeles Metropolitan Statistical Area for 1992. As can be seen from the Figure, the comparison is quite good, giving good validation for the statistical physics constrained optimization applicability to an economics problem. In this preliminary study, the interest was in the impact of information technology (IT) on various statistical physics parameters of companies as a function of company size. Table 3 shows the ratios of the 1997 statistical physics characterization parameters to their values in


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1992 for large, intermediate, and small companies in the Los Angeles manufacturing sectors. Shown are the ratios for the number of companies in a size segment (#). the number of employees in the corresponding segment (E), the average number of employees per company (E/comp). the shipments of the sector (Sh), the shipments per employee (Sh/E), and a normalized “inverse temperature (β)

Table 3. Ratio (“97/’92) of the statistical physics characterization parameters (from Dozier & Chang , 2006b) Without going into detail, it can be seen from Table 3 that the impact of information technology investments on the statistical physics characterization parameters does seem to be correlated with the company size. Whether or not the same type of size-dependency exists in the technology transfer realm bears analysis. To summarize, Section 2 has shown that the application of the constrained optimization technique of statistical physics to (quasi) time-independent economic phenomena is supported by preliminary comparisons with U.S. Economic Census Data for the Los Angeles Metropolitan Statistical Area. The application of the technique appears to be justified both to systematically analyze the data and to provide a comprehensive and believable framework for presenting the results. For our immediate purpose of analyzing oscillatory phenomena in supply chains, the successes also provide confidence in the concept of an effective pseudo-thermodynamic-derived “information force”.

3. Normal mode oscillations in supply chains In this section, we describe a simple statistical physics model of inventory oscillations in a supply chain. It is based on material presented in three conferences (Dozier & Chang, 2005, 2006a, 2007), and follows especially closely the presentation in Dozier & Chang (2006a) Section 3a describes a simple time-dependent conservation equation for the production units flowing through the chain. This equation is the supply-chain equivalent of the Liouville or Vlasov equations of statistical physics. Section 3b applies this equation to derive results for a chain in which each entity only exchanges information with the two entities immediately above and below it in the chain. Section 3c derives the oscillation dispersion relation for a supply chain in which every entity in the chain exchanges information with all the other entities in the chain. In order to focus on the bare essentials of the approach, only a linear supply chain will be considered in the following. In addition, end effects will be ignored. It should be quite apparent how to generalize the results to a situation in which the number of companies at each level of the supply chain varies, and in which effects associated with the finite length of a chain are included.

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3.1 Conservation equation for supply chain In order to replace difference equations with more familiar differential equations, instead of designating each level in the chain by a discrete label n, the position in a chain will be designated by a continuum variable x. (Dozier & Chang (2005) shows the modifications resulting from dealing with the discrete label n instead of the continuous variable x, and demonstrates that for long supply chains, the basic features of both treatments are essentially the same.) The rate of flow of a production unit through each position x in the chain can be characterized by a velocity variable v. Introduce a distribution function f(x,v,t) that depends on position, production flow rate velocity, and time. In statistical physics parlance, x and v denote the phase space for the problem, and f(x,v,t)dxdv denotes the number of production units in the intervals dx and dv at a given x and v at the time t. From its definition, this distribution function satisfies a conservation equation in the phase space of x and v, if it is assumed that no production units are destroyed at each level of the chain: ∂f/∂t + ∂[fdx/dt]/∂x +∂[fdv/dt]/∂v = 0

(1)

This equation simply states that the change of fdxdv is due only to the divergence of the flow into the phase space volume dxdv. In a perfectly operating supply chain, we would expect that there would be no divergence in the flow. By permitting a divergence in the flow – i.e. permitting the flow into a volume element dxdv to be different than the flow out, the possible existence of local inventory fluctuations is allowed. By introducing a force F that influences the velocity of the production rate v, this equation can be rewritten ∂f/∂t + ∂[fv]/∂x +∂[fF]/∂v = 0

(2)

We assume that the force F is of the type discussed earlier in Section 2b(i), i.e. that it can be uniquely derived from a pseudo-thermodynamic partition function for the chain entities. Since position x and velocity of the production rate v are independent variables, ∂v/∂x = 0. If, moreover, the force F does not depend on the flow rate v, ∂F/∂v = 0, so that Eq. (2) becomes ∂f/∂t + v∂f/∂x + F∂f/∂v = 0

(3)

This is similar to the Liouville equation of classical mechanics, and has the familiar form of the Vlasov equation for collisionless plasmas (Spitzer, 1956). 3.2 Supply chain with local exchange of information Now assume that F at the position x is determined only by the level of the inventories of the production units immediately above and below x in the chain. As explained below, it is reasonable to assume that the fractional change in the time rate of change of velocity (1/v)dv/dt is proportional to the fractional change in the gradient of the density N(x,t): (1/v)dv/dt ∝ - (1/N)dN/dx

(4)

Where N(x,t) = ∫dvf(x,v,t), and where the negative sign is explained below. For local information exchange with the levels immediately above and below the level of interest, the change in the density is observed over only the interval dx =2l, where l is the spacing between levels in the supply chain. Thus, we can further write


(1/v)dv/dt ∝ - (21/N)dN/dx

171 (5)

The rationale for this expression is that when the inventory of the level below the level of interest is less than normal, the production rate (v) will be diminished because of the smaller number of production units being introduced to that level. At the same time, when the inventory of the level above the level of interest is larger than normal, the production rate will also be diminished because the upper level will demand less input so that it can “catch up” in its production through-put. Both effects give production rate changes proportional to the negative of the gradient of N. It is reasonable also that the fractional changes are related rather than the changes themselves, since deviations are always made from the inventories at hand. We note in passing that the quantity l is somewhat arbitrary, and reflects an equally arbitrary choice of a scale factor that relates the continuous variable x and the discrete level variable n. A time scale for the response is missing from Eq. (5). We know that a firm must make decisions on how to react to the flow of production units into the firm. Assume that the time scale of response τresponse is given by τresponse = (1/ξ)τprocessing, where τprocessing is the processing time for a unit as it passes through the firm, and for simplification we are assuming ξ and τprocessing are constant throughout the chain. Because of a natural inertia associated with cautious decision-making, it is likely that ξ will be less than unity, corresponding to response times being longer than processing times. Then Eq. (5) becomes (1/v)dv/dt = - (2ξ1/τprocessing N)dN/dx

(6)

Since by definition, the steady state production rate velocity is given by V0 ≈ l/τprocessing, this gives finally for the effective internal force that changes production flow rates: F = dv/dt = - 2ξ V02(1/N)dN/dx

(7)

Insertion of this expression into Eq. (3) then yields ∂f/∂t + v∂f/∂x - 2ξ V02(1/N)(dN/dx) ∂f/∂v = 0

(8)

In the steady state, the equation is satisfied by f(x,v,t) = f0(v), i.e. by a distribution function that is independent of position and time: In this desired steady state, production units flow smoothly through the line without bottlenecks. For a smoothly operating supply chain, f0(v) will be centered about the steady state flow velocity V0, a fact that we shall make use of later. Now suppose there is a (normal mode) perturbation of the form exp[i(ωt – kx)], i.e. f(x,v,t) = f0(v) + f1(v) exp[-i(ωt – kx)]

(9)

On linearizing eq. (8) with this f(x,v,t), we find that f1(v) satisfies -i(ω-kv)f1 - ik 2ξ V02(1/N0)N1∂f0/∂v = 0

(10)

f1 = -2ξk(N1/N0) V02∂f0/∂v(ω-kv)-1

(11)

Solving for f1:

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On integrating this equation with respect to v, we get the statistical physics dispersion relation relating ω and k: 1+ 2ξkV02(1/N0) ∫dv∂f0/∂v(ω-kv)-1 =0

(12)

This equation contains a singularity at ω=kv. This singularity occurs where the phase velocity ω/k becomes equal to the velocity of flow v. There are well-defined methods for the treatment of singularities: Following the Landau prescription (Landau, 1946; Stix,1992) ∫dv∂f0/∂v(ω-kv)-1 = PP∫dv∂f0/∂v(ω-kv)-1 - iπ(1/k)∂f0(ω/k) /∂v

(13)

where PP denotes the principal part of the integral, i.e. the value of the integral ignoring the contribution of the singularity. To evaluate the principal part, assume that for most v, ω>>kv. Then approximately PP∫dv∂f0/∂v(ω-kv)-1≈ ∫dvkv∂f0/∂v(1/ω2) ≈ - kN0 /ω2

(14)

This gives the sound-wave-like dispersion relation ω ≈ (2ξ)1/2 kV0

(15)

Addition to this of the small contribution from the imaginary part yields ω = (2ξ)1/2 kV0+ (k/2)(1/N0)iπ(ω/k)2∂f0(ω/k) /∂v

(16)

or, on using the approximate relationship of Equation [21] for the ω’s in the second term on the RHS ω = (2ξ)1/2 kV0 + i(π/2)(k/N0) (2ξ)3/2 V0 3∂f0((2ξ)1/2 V0 ) /∂v]

(17)

For the fast response times made possible by first order rapid information exchange, ξ = O(1). Thus, with f0(v) peaked around V0, ∂f0(4ξV0 ) /∂v > V0 (where the distribution is peaked), the derivative will be small, however, and the damping will be correspondingly small. We note in passing that the discrete level variable is used instead of the continuous variable x, the dispersion relation is the same as Eq. (10) for small k, but when kl → 1, the dispersion relation resembles that of an acoustic wave in a solid (Dozier & Chang, 2004, and Kittel, 1996). To summarize, this sub-section has shown that when an entity in this linear supply chain exchanges information only with the two entities immediately above and below it in the chain, a slightly damped sound-wave-like normal mode results. Inventory disturbances in such a chain tend to propagate forwards and backwards in the chain at a constant flow velocity that is related to the desired steady-state production unit flow velocity through the chain. 3.3 Supply chain with universal exchange of information Consider next what happens if the exchange of information is not just local. (Suppose that information is shared equally between all participants in a supply chain such as in the use of

173


grid computing.) In this case, the force F in Eq. (3) is not just dependent on the levels above and below the level of interest, but on the f(x,v,t) at all x. Let us assume that the effect of f(x,v,t) on a level is independent of what the value of x is. This can be described by introducing a potential function Φ that depends on f(x,v,t,) by the relation ∂2Φ/∂x2 = - [C/N0]∫dv f(x,v,t)

(18)

from which the force F is obtained as F = - ∂Φ/∂x. (That this is so can be seen by the form of the 1-dimensional solution to Poisson’s equation for electrostatics: the corresponding field from a source is independent of the source position.) The constant C can be determined by having F reduce approximately to the expression of Eq. (7) when f(x,v,t) is non zero only for the levels immediately above and below the level x0 of interest in the chain. For that case, take N(x+l) = N(x0) +dN/dx l and N(x- l) = N(x0) dN/dx l, and N(x) zero elsewhere. Then F = - ∂Φ/∂x = - [C/N0](dN/dx) 2l2 2ξv2(1/N)dN/dx,

On comparing this with the F of Eq. (7), F = distribution function is peaked at V0) that we can write C = ξV02 / l2 . Accordingly,

(19) we find (since the

∂2Φ/∂x2 = - [ξV02 /N0l2 ]∫dv f(x,v,t)

(20)

With these relations, F from the same value of f(x,v,t) at all x above the level of interest is the same, and F from the same value of f(x,v,t) at all x below the level of interest is the same but of opposite sign. This is the desired generalization from local information exchange to universal information exchange. It is interesting to see what change this makes in the dispersion relation. Eq. (3) now becomes ∂f/∂t + v∂f/∂x - ∂Φ/∂x ∂f/∂v = 0

(21)

and again the dispersion relation can be obtained from this equation by introducing a perturbation of the form of Equation (15) and assuming that Φ is of first order in the perturbation. This gives -i(ω-kv)f1 = ikΦ1∂f0 /∂v

(22)

f1 = -kΦ1∂f0 /∂v (ω-kv)-1

(23)

Φ1 = (1/k2) [ξV02 /N0l2] ∫dv f1(v)

(24)

i.e., Since Eq. (20) implies

we get on integrating Eq. (23) over v: 1+ (1/k) [ξV02 /N0l2]∫dv∂f0 /∂v (ω-kv)-1 = 0

(25)

Once again a singularity appears in the integral, so we write ∫dv∂f0 /∂v (ω-kv)-1= PP∫dv∂f0 /∂v (ω-kv)-1- iπ(1/k)∂f0(ω/k) /∂v

(26)

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Evaluate the principal part by moving into the frame of reference moving at V0, and in that frame assume that kv/ω0, discarding the minus solution for this case. Now add the small imaginary part to the integral: 1+ (1/k) [ξV02 /N0l2][ -kN0/(ω-kV0)2 - iπ(1/k)∂f0(ω/k) /∂v]= 0

(31)

On iteration, this yields ω≈ kV0 + ξ1/2(V0/l) [1 + i {πξV02/(2k2l2N0)}∂f0/∂v ]

(32)

where ∂f0/∂v is evaluated at v = ω/k ≈ V0 + (ξ1/2V0/kl). Since for velocities greater than V0 , ∂f0/∂v< 0, we see that the oscillation is damped. Moreover, the derivative ∂f0/∂v is evaluated at a velocity close to V0, the flow velocity where the distribution is maximum. Since the distribution function is larger there, the damping can be large. (We note here that the expression of Eq. (32) differs a little from that in Dozier & Chang (2006a), due to an algebraic error in the latter.) To summarize, Section 3 has shown that universal information exchange results both in changing the form of the supply chain oscillation to a plasma-like oscillation, and in the suppression of the resulting oscillation. Specifically, it has been shown that for universal information exchange, the dispersion relation resembles that for a plasma oscillation. Instead of the frequency being proportional to the wave number, as in the local information exchange case, the frequency now contains a component which is independent of wave number. The plasma-like oscillations for the universal information exchange case are always damped. As the wave number k becomes large, the damping (which is proportional to ∂f0 (ω/k) /∂v) can become large as the phase velocity approaches closer to the flow velocity V0. This supports Sterman and Fiddaman’s conjecture that IT will have beneficial effects on supply chains.

4. External interventions that can increase supply chain production rates In Section 3, we have seen that universal information exchange among all the entities in a supply chain can result in damping of the undesirable supply chain oscillations. In this


175

section, we change our focus to see if external interactions with the oscillations can be used to advantage to increase the average production rate of a supply chain. A quasilinear approximation technique has been used in plasma physics to demonstrate that the damping of normal mode oscillations can result in changes in the steady state distribution function of a plasma. In this section, this same technique will be used to demonstrate that the resonant interactions of externally applied pseudo-thermodynamic forces with the supply chain oscillations also result in a change in the steady state distribution function describing the chain, with the consequence that production rates can be increased. This approach will be demonstrated by using a simple fluid flow model of the supply chain, in which the passage of the production units through the supply chain will be regarded as fluid flowing through a pipe. This model also gives sound-like normal mode waves, and shows that the general approach is tolerant of variations in the specific features of the supply chain model used. A more detailed treatment of this problem is available at Dozier and Chang (2007). 4.1 Moment equations and normal modes The starting point is again the conservation equation, Eq. (5), for the distribution function that was derived in Section 3a. To obtain a fluid flow model of the supply chain, it will be useful to take various moments of the distribution function: Thus, the number of production units in the interval dx and x at time t, is given by the v0 moment, N(x, t) = ∫dvf(x,v,t); and the average flow fluid flow velocity is given by the v1 moment V(x,t) = (1/N)∫ vdvf(x,v,t). By taking the v0 and v1 moments of Eq. (3) – see, e.g. Spitzer (2006) - we find ∂N/∂t + ∂[NV]/∂x = 0

(33)

∂V/∂t +V∂V/∂x = F1 - ∂P/∂x

(34)

and

where F1(x,t) is the total force F acting per unit dx and P is a “pressure” defined by taking the second moment of the dispersion of the velocities v about the average velocity V: P(x,t) = ∫dv(v-V)2 f(x,v,t) We can write the pressure P in the form P(x,t) = ∫dv(v-V)2 f(x,v,t) = N(x,t) (Δv)2

(35)

(Δv)2 = ∫dv(v-V)2 f(x,v,t)/N(x,t)

(36)

where

This is a convenient form, since we it shall assume for simplicity that the velocity dispersion (Δv)2 is independent of level x and time t. In that case, Eq. (34) can be rewritten as ∂V/∂t +V∂V/∂x = F1 - (Δv)2 ∂N/∂x

(37)

This implies the change in velocity flow is impacted by the primary forcing function and the gradients of the number density of production units. Equations (33) and (37) are the basic equations that we shall use in the remainder to describe temporal phenomena in this simple fluid-flow supply chain model.

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Before considering the effect of externally applied pseudo-thermodynamic forces, we derive the normal modes for the fluid flow model. Accordingly, introduce the expansions N(x,t) = N0 +N1(x,t) and V(x,t) = V0 + V1(x,t) about the level- and time-independent steady state density N0 and velocity V0. (We can take the steady state quantities to be independent of the level in the supply chain, since again we are considering long supply chains in the approximation that end effects can be neglected.) Upon substitution of these expressions for N(x,t) and V(x,t) into Eqs. (33) and (37), we see that the lowest order equations (for N0 and V0) are automatically satisfied, and that the first order quantities satisfy ∂N1 /∂t + V0 ∂N1 /∂x + N0 ∂V1 /∂x = 0

(38)

∂V1 /∂t +V0 ∂V1 /∂x = F1(x,t) - (Δv)2 ∂N1 /∂x

(39)

and

where F1(x,t) is regarded as a first order quantity. As before,. the normal modes are propagating waves: N1(x,t) = N1exp[i(ωt -kx)]

(40)

V1(x,t) = V1exp[i(ωt -kx)]

(41)

With these forms, Eqs. (38) and (39) become i (ω-kV0)N1 + N0 ikV1 = 0

(42)

i N0 (ω-kV0)V1 = -ik (Δv)2N1

(43)

In order to have nonzero values for N1 and V1, these two equations require that (ω-kV0)2 = k2(Δv)2

(44)

Equation (44) has two possible solutions ω+ = k (V0 + Δv)

(44a)

ω- = k (V0 - Δv)

(44b)

The first corresponds to a propagating supply chain wave that has a propagation velocity equal to the sum of the steady state velocity V0 plus the dispersion velocity width Δv. The second corresponds to a slower propagation velocity equal to the difference of the steady state velocity V0 and the dispersion velocity width Δv. Both have the form of a sound wave: if there were no fluid flow (V0 = 0), ω+ would describe a wave traveling up the chain, whereas ω- would describe a wave traveling down the chain. When V0 ≠ 0, this is still true in the frame moving with V0 4.2 Resonant interactions resulting in an increased production rate As indicated earlier, our focus in this section is on the effect of external interactions (such as government actions) on the rate at which an evolving product moves along the supply chain. This interaction occurs in the equations through an effective pseudo-thermodynamic


177

force F1(x,t) that acts to accelerate the rate. From the discussion of Section 3, we expect that this force will be most effective when it has a component that coincides with the form of a normal mode, since then a resonant interaction can occur. To see this resonance effect, it is useful to present the force F in its Fourier decomposition F1(x,t) = (1/2π)∫∫dωdkF1(ω,k)exp[i(ωt-kx)]

(45)

F1(ω,k) = (1/2π)∫∫dxdtF1(x,t)exp[-i(ωt-kx)]

(46)

where

With this Fourier decomposition, each component has the form of a propagating wave, and it would be expected that these propagating waves are the most appropriate quantities for interacting with the normal modes of the supply chain. Our interest is in the change that F1 can bring to V0, the velocity of product flow that is independent of x. By contrast, F1 changes V1 directly, but each wave component causes an oscillatory change in V1 both in time and with supply chain level, with no net (average) change. To obtain a net change in V, we shall go to one higher order in the expansion of V(x,t): V(x,t) = V0 + V1(x,t)+ V2(x,t)

(47)

On substitution of this expression into Eq. (37), we find the equation for V2(x,t) to be N0(∂V2/ ∂t + V0∂V2/∂x) + N1(∂V1/ ∂t + V0∂V1/∂x) + N0 V1∂V1/∂x = - (Δv)2 ∂N2 /∂x (48) This equation can be Fourier analyzed, using for the product terms the convolution expression: ∫∫dxdt exp[-i(ωt-kx)] f(x,t)g(x,t) = ∫∫dΩdK f(-Ω+ω, -K+k)g(Ω,K)

(49)

f(Ω,K) = ∫∫dxdt exp[-i(Ωt-Kx)]f(x,t)

(50a)

g(Ω,K) = ∫∫dxdt exp[-i(Ωt-Kx)]g(x,t)

(50b)

where

Since we are interested in the net changes in V2 – i.e. in the changes brought about by F1 that do not oscillate to give a zero average, we need only look at the expression for the time rate of change of the ω=0, k=0 component, V2(ω=0, k=0). From Eq. (48), we see that the equation for ∂ V2(ω=0, k=0)/∂t requires knowing N1 and V1. When F1(ω,k) is present, then Eqs. (42) and (43) for the normal modes are replaced by i (ω-kV0)N1(ω,k) + N0 ikV1(ω,k) = 0

(51)

i N0 (ω-kV0)V1(ω,k) = -ik (Δv)2N1(ω,k) + F1(ω,k)

(52)

These have the solutions N1(ω,k) = -ik F1(ω,k) [(ω-kV0)2 – k2 (Δv)2]-1

(53)

V1(ω,k) = - i {F1(ω,k)/N0}(ω-kV0) [(ω-kV0)2 – k2 (Δv)2]-1

(54)

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Substitution of these expressions into the ω=0, k=0 component of the Fourier transform of Eq. (48) gives directly ∂ V2(0,0)/∂t = ∫∫dωdk(ik/N02) (ω-kV0)2 [(ω-kV0)2 – k2 (Δv)2]-2 F1(-ω,k) F1(-ω,k)

(55)

This resembles the quasilinear equation that has long been used in plasma physics to describe the evolution of a background distribution of electrons subjected to Landau acceleration [Drummond & Pines (1962)]. As anticipated, a resonance occurs at the normal mode frequencies of the supply chain, i.e. when (ω-kV0)2 – k2 (Δv)2 = 0

(56)

First consider the integral over ω from ω = -∞ to ω = ∞. The integration is uneventful except in the vicinity of the resonance condition where the integrand has a singularity. As before, the prescription of Eq. (13) can be used to evaluate the contribution of the singularity. For Eq. (55), we find that when the bulk of the spectrum of F1(x,t) is distant from the singularities, the principal part of the integral is approximately zero, where the principal part is the portion of the integral when ω is not close to the singularities at ω = k(V0± Δv). This leaves only the singularities that contribute to ∂V2(0,0)/∂t . The result is the simple expression: ∂V2(0,0)/∂t = π/(N02Δv) ∫dk(1/k) [ F1(-k(V0- Δv, -k)F1(k(V0- Δv),k) – (-k(V0+ Δv, -k)F1(k(V0+Δv),k)]

(57)

Equation (57) suggests that any net change in the rate of production in the entire supply chain is due to the Fourier components of the effective statistical physics force describing the external interactions with the supply chain, that resonate with the normal modes of the supply chain. In a sense, the resonant interaction results in the conversion of the “energy” in the normal mode fluctuations to useful increased production flow rates. This is very similar to physical phenomena in which an effective way to cause growth of a system parameter is to apply an external force that is in resonance with the normal modes of the system. To summarize, Section 4 has shown that the application of the quasilinear approximation of statistical physics to a simple fluid-flow model of a supply chain, demonstrates how external interactions with the normal modes of the chain can result in an increased production rate in the chain. The most effective form of external interaction is that which has Fourier components that strongly match the normally occurring propagating waves in the chain.

5. Discussion and possible extensions In the foregoing, some simple applications of statistical physics techniques to supply chains have been described. Section 2 briefly summarized the application of the constrained optimization technique of statistical physics to (quasi) time-independent economic phenomena. It showed some preliminary comparisons with U.S. Economic Census Data for the Los Angeles Metropolitan Statistical Area, that supported the approach as a good means of systematically analyzing the data and providing a comprehensive and believable framework for presenting the results. It also introduced the concept of an effective pseudo-thermodynamic-derived “information force” that was used later in the discussion of supply chain oscillations.


179

Section 3 discussed supply chain oscillations using a statistical physics normal modes approach. It was shown that the form of the dispersion relation for the normal mode depends on the extent of information exchange in the chain. For a chain in which each entity only interacts with the two entities immediately below and above it in the chain, the normal more dispersion relation resembles that of a sound wave. For a chain in which each entity exchanges information with all of the other entities in the chain, the dispersion relation resembles that of a plasma oscillation. The Landau damping in the latter could be seen to be larger than in the limited information exchange case, pointing up the desirability of universal information exchange to reduce undesirable inventory fluctuations. Section 4 applied the quasilinear approximation of statistical physics to a simple fluid-flow model of a supply chain, to demonstrate how external interactions with the normal modes of the chain can result in an increased production rate in the chain. The most effective external interactions are those with spectra that strongly match the normally occurring propagating waves in the chain. The foregoing results are suggestive. Nevertheless, the supply chain models that were used in the foregoing were quite crude: Only a linear uniform chain was considered, and end effects were ignored. There are several ways to improve the application of statistical physics techniques to increase our understanding of supply chains. Possibilities include (1) the allowance of a variable number of entities at each stage of the chain, (2) relaxation of the uniformity assumption in the chain, (3) a more comprehensive examination of the effects of the time scales of interventions, (4) a systematic treatment of normal mode interactions, (5) treatment of end effects for chains of finite length,(6) consideration of supply chains for services as well as manufactured goods, and (7) actual simulations of the predictions. We can briefly anticipate what each of these extensions would produce. Variable number of entities at each level Equations similar to those in Sections 3 and 4 would be anticipated. However, in the equations, the produced units at each level would now refer to those produced by all the organizations at that particular level. The significance is that the inventory fluctuation amplitudes calculated in the foregoing refer to the contributions of all the organizations in a level, with the consequence that the fluctuations in the individual organization would be inversely proportional to the number of entities in that level. Thus, organizations in levels containing few producing organizations would be expected to experience larger inventory fluctuations. Nonuniform chains In Sections 3 and 4, it was assumed that parameters characterizing the processing at each level (such as processing times) were uniform throughout the chain. This could very well be unrealistic: for example, some processing times at some stages could be substantially longer than those at other stages. And in addition, the organizations within a given stage could very well have different processing parameters. This would be expected both to introduce dispersion, and to cause a change in the form of the normal modes. As a simple example, suppose the processing times in a change increased (or decreased) linearly with the level in the chain. The terms of the normal mode equation would now no longer have coefficients that were independent of the level variable x. For a linear dependence on x, the normal modes change from Fourier traveling waves to combinations of Bessel functions, i.e. the normal mode form for a traveling wave is now a Hankel function. The significance of this is that the inventory fluctuation amplitudes become level-

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dependent: A disturbance introduced at one level in the chain could produce a much larger (smaller) fluctuation amplitude at another level. Time scales of interventions Since inventory fluctuations in a supply chain are disruptive and wasteful of resources, some form of cybernetic control (intervention) to dampen the fluctuations would be desirable. In Section 4, it was suggested that interventions that resonate with the normal modes are most effective in damping the fluctuations and converting the “energy” in the fluctuations to useful increased production rates. Koehler (2001, 2002) has emphasized, however, that often the time scales of intervention are quite different from those of the system whose output it is desired to change. A systematic means of analyzing the effects of interventions with time scales markedly different from those of the supply chain is available with standard statistical physics techniques: For example, if the intervention occurs with a time scale much longer than the time scales of the chain’s normal modes, then the adiabatic approximation can be made in describing the interactions. The intervention can be regarded as resulting in slowly changing parameters (as a function of both level and time). Eikonal equations (Weinberg 1962) can then be constructed for the chain disturbances, which now can be regarded as the motion of “particles” comprising wave packets formed from the normal modes. At the other extreme, suppose the intervention occurs with time scales much less than the time scales of the chain’s normal modes. When the intervention occurs at random times, the conservation equation (Eq. 3) can be modified by Fokker-Planck terms (Chandrasekhar, 1943). The resulting equation describes a noisy chain, in which a smooth production flow can be disrupted. Normal mode interactions The beer distribution simulation (Sterman & Fiddaman, 1993) has shown that the amplitudes of the inventory oscillations in a supply chain can become quite large. The normal mode derivation in Sections 3 and 4 assumed that the amplitudes were small, so that only the first order terms in the fluctuation amplitudes needed to be kept in the equations. When higher order terms are kept, then the normal modes can be seen to interact with one another. This “wave-wave” interaction itself can be expected to result in temporal and spatial changes of the supply chain inventory fluctuation amplitudes. End effects of finite chains The finite length of a supply chain has been ignored in the calculations of this chapter, i.e. end effects of the chain have been ignored. As in physical systems, the boundaries at the ends can be expected to introduce both reflections and absorption of the normal mode waves described. These can lead to standing waves, and the position and time focus of optimal means of intervention might be expected to be modified as a result. Supply chains for services as well as manufactured products In the foregoing, we have been thinking in terms of a supply chain for a manufactured product. This supply chain can involve several different companies, or – in the case of a vertically integrated company – it could comprise several different organizations within the company itself. The service sector in the economy is growing ever bigger, and supply chains can also be identified, especially when the service performed is complex. The networks involved in service supply chains can have different architectures than those for manufacturing supply chains, and it will be interesting to examine the consequences of this difference. The same type of statistical physics approach should prove useful in this case as well. Numerical simulations The statistical physics approach to understanding supply chain oscillations can lead to many types of predicted effects, ranging from the form and


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frequencies of the inventory fluctuations to the control and conversion of the fluctuations. Computer simulations would be useful in developing an increased understanding of the predictions. This is especially true when the amplitudes of the oscillations are large, since then the predictions based on small-amplitude approximations would be suspect. The application of statistical physics techniques to understand and control supply chain fluctuations may prove to be very useful. The initial results reported here suggest that further efforts are justified.

6. References Bogolyubov, N.N., and Zubarev, D.N..(1955) An Asymptotic Approximation Method for a System with Rotating Phases and its Application to the Motion of a Charged Particle in a Magnetic Field, Ukrainian Math. Journal VI,I 5, ISSN 0041-5995 Chandrasekhar, S. (1943) Stochastic Processes in Physics and Astronomy, Revs. Modern Physics 15, 1-89, ISSN 1539-0756 Chang, David B. (1964) Landau Damping and Related Phenomena, Phys. Fluids 7, 1980-1986, ISSN 1070-6631 Costanza, R., Cumberland, J., Daly, H., Goodland, R., Norgaard, R. (1997) An Introduction to Ecological Economics. ISBN 1884015727, Saint Lucie Press, Boca Raton, Florida Dozier, K., and Chang, D. (2004a) Thermodynamics of Productivity: Framework for Impacts of Information/Communication Investments, International Conference on Cybernetics and Information Technologies, Systems and Applications (CITSA 2004), ISBN 980-6560-19-1, Orlando, Florida, July 21-25, 2004, International Institute of Informatics and Systemics, Winter Garden, Florida Dozier, K., and Chang, D. (2004b) A Thermodynamic Model for Technology Transfer, presentation at Technology Transfer Society (T2S) Annual Conference: Emerging Issues in Technology Transfer, RPI , September 29-October 1, 2004, Albany , NY, available at www.wesrac.usc.edu Dozier, K., and Chang, D. (2005) Cybernetic control in a supply chain: wave propagation and resonance. The 11th International Conference on Information Systems Analysis and Synthesis. (ISAS 2005) and the 2nd International Conferenc4 on Cybernetics and Information Technologies, Systems and Applications (CITSA 2005). July 14-17, 2005, Orlando, Florida, ISBN 980-6560-41-8 and 980-6560-42-6, International Institute of Informatics and Systemics, Winter Garden, Florida Dozier, K., and Chang, D. (2006a) Role of information exchange in damping supply chain oscillations. Information Resources Management Association (IRMA) conference, ISBN 1-59904-020-4, Washington, D.C., May 21-24, 2006, Information Resources Management Association, Hershey, PA Dozier, K., and Chang, D. (2006b) The effect of company size on the productivity impact of information technology investments. Journal of Information Technology Theory and Application (JITTA) 8:1, ISSN 1532-4516 Dozier, K., and Chang, D. (2007) The impact of information technology on the temporal optimization of supply chain performance. Proceedings of the Hawaii International Conference on System Sciences HICSS-40, 2007, ISBN 0-7695-2755-8, January 3-6, 2007, Hilton Waikoloa Village, Big Island, Hawaii, IEEE Computer Society, Washington, D.C..

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Drummond, W.E. and Pines, D. (1962) Non-linear stability of plasma oscillations, Nucl. Fusion, Suppl, 3, 1049-1057, ISSN 0029-5515 Kittel, Charles (1996) Introduction to Solid State Physics. ISBN 047111813, John Wiley & Sons, New York Koehler, Gus (2003) Time, Complex Systems, and Public Policy: A Theoretical Foundation for Adaptive Policy Making, Nonlinear Dynamics, Psychology and the Life Sciences 7, 1, 99, January, 2003, ISSN 1090- 0578 Koehler, Gus, (2001) A Framework for Visualizing the Chronocomplexity of Politically Regulated Time- Ecologies, presentation at International Society for the Study of Time 2001 Conference, Gorgonza, Italy, July 8-22, 2001, available at www.timestructures.com/ downloads/ISST-Arrowhead%20PowerPt.pdf Krugman, Paul (1995) Development, Geography, and Economic Theor. ISBN 0585003246, The MIT Press , Cambridge, Mass. Landau, L.D. (1946) On the Vibrations of the Electronic Plasma. J. Phys. (U.S.S.R.) 10, 25-34, ISSN 0368-3400 Smith, E. and Foley, D. (2002) Classical thermodynamics and economic general equilibrium theory. 32 page working paper, New School for Social Research, New York, available at Duncan K. Foley homepage Spitzer, L. (2006). Physics of Fully Ionized Gases. ISBN 0486449823, Dover Publications, Mineola, New York Sterman, J.D. & Fiddaman, T. (1993). The Beer Distribution Game Flight Simulator, Software (for Macintosh) and Briefing Book. Sloan School of Management, MIT E53-351 Cambridge, MA 02142 Stix, T.H. (1992) Waves in Plasmas, ISBN 0883188597, American Institute of Physics, New York Thome, F. A., and London, S. (2000). Disequilibrium economics and development. Computing in Economics and Finance 2000, paper 377, repec.sce.scecf0, Society for Computational Economics, Barcelona, Spain Weinberg, S. (1962) Eikonal method in magnetohydrodynamics. Phys. Rev. 126, 1899-1909, ISSN 1550-2376

10 Utilizing IT as an Enabler for Leveraging the Agility of SCM Mehdi Fasanghari and S. K. Chaharsooghi

Iran Telecommunication Research Center (ITRC) & Tarbiat Modares University (TMU) Iran 1. Introduction Supply chain management (SCM) is the 21st century operations strategy for achieving organizational competitiveness. Companies are attempting to find ways to improve their flexibility, responsiveness, and competitiveness by changing their operations strategy, methods, and technologies that include the implementation of SCM paradigm and Information Technology (IT). The use of IT is considered as a prerequisite for the effective control of today’s complex supply chains. Indeed, a recent study is increasingly dependent on the benefits brought about by IT to: improve supply chain agility, reduce cycle time, achieve higher efficiency, and deliver products to customers in a timely manner (Radjou, 2003). However, IT investment in the supply chain process does not guarantee a stronger organizational performance. The debate on the ‘‘IT-productivity’’ paradox and other anecdotal evidence suggests that the impact of IT on firm performance remains unclear (Lucas & Spitler, 1999). In fact, the adoption of a particular technology is easily duplicated by other firms, and it often does not provide a sustained competitive advantage for the adopting firms (Powell & Dent-Micallef, 1997). The implementation of IT in the SCM can enable a firm to develop and accumulate knowledge stores about its customers, suppliers, and market demands, which in turn influences firm performance (Tippins & Sohi, 2003). The main objective of this paper is to provide a framework that enhances the agility of SCM with IT. The rest of this article is organized as follows. IT systems and Supply Chain Management will be described in the next sections. Therefore we begin with a brief review of the IT and SCM. Definitions for agility–as key subjects in this article- are ambiguous. Then, leveraging the agility of SCM is argued and the framework is represented. This is ended by conclusion.

2. IT systems As for IT systems, when discussing the use of IT in SCM, we refer to the use of interorganizational systems that are used for information sharing and/or processing across organizational boundaries. Thus, besides internal IT systems such as Enterprise Resource Planning systems we also consider identification technologies such as RFID from the scope of this study (Auramo et al., 2005).

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3. Supply chain management A business network is defined as a set of two or more connected business relationships in which exchange in one relationship is contingent on (non-) exchange in another (Campbell & Wilson, 1996). Stevens (1989) defines SCM as ‘a series of interconnected activities which are concerned with planning, coordinating and controlling materials, parts, and finished goods from supplier to customer. A supply chain typically consists of the geographically distributed facilities and transportation links connecting these facilities. In manufacturing industry this supply chain is the linkage which defines the physical movement of raw materials (from suppliers), processing by the manufacturing units, and their storage and final delivery as finished goods for the customers. In services such as retail stores or a delivery service like UPS or Federal Express, the supply chain reduces to problem if distribution logistics, where the start point is the finished product that has to be delivered to the client in a timely, manner. For a pure service operation, such as a financial services firm or a consulting operation, the supply chain is principally the information flow (Bowersox & Closs, 1996). SCM and logistics definitions entail a supply chain perspective from first supplier to enduser and a process approach, but the main difference between them is that Logistics is a subset of SCM. Companies have realized that all business processes along with logistics process cut across supply chains (Lambert & Cooper, 1998). According to that, SCM ideally embraces all business processes cutting across all organizations within the supply chain, from initial point of supply to the ultimate point of consumption (Lambert & Cooper, 1998). For, SCM embraces the business processes identified by the International Center for Competitive Excellence (see Fig. 1).

4. IT and supply chain management Recently with development of information technologies that include electronic data interchange (EDI), the Internet and World Wide Web (WWW), the concepts of supply chain design and management have become a popular operations paradigm. The complexity of SCM has also forced companies to go for online communication systems. For example, the Internet increases the richness of communications through greater interactivity between the firm and the customer (Walton & Gupta, 1999). Armstrong & Hagel (1996) argue that there is beginning of an evolution in supply chain towards online business communities. Supply chain management emphasizes the long-term benefit of all parties on the chain through cooperation and information sharing. This signifies the importance of communication and the application of IT in SCM. This is largely caused by variability of ordering (Yu et al., 2001). There have been an increasing number of studies of IT’s effect on supply chain and interorganizational relationships (Grover et al., 2002). In this article, IT appears to be an important factor for collaborative relationships. A popular belief is that IT can increases the information processing capabilities of a relationship, thereby enabling or supporting greater interfirm cooperation in addition to reducing uncertainty (Subramani, 2004). IT decreases transaction costs between buyers and suppliers and creates a more relational/cooperative governance structure, leads to closer buyer-supplier relationships (Bakos & Brynjyoolfsson, 1993), may decrease trust-based interorganizational partnerships and removes a human element in buyer-supplier interaction, while trust is built on human interaction (Carr &

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Utilizing IT as an Enabler for Leveraging the Agility of SCM

Smeltzer, 2002). A new challenge of marketing is occurred with combination of e-business and SCM. IT allows suppliers to interact with customers and receive enormous volumes of information for data mining and knowledge extraction; this knowledge help suppliers for better relationship with their customers (Zhang, 2007). Network Integration in e-business environment increase the flexibility and link the suppliers and customers electronically based on three basic components (Poirier & Bauer, 2000): e-network (for satisfying the customer demands through a seamless supply chain), responses (for integrating interenterprise solutions and responses and customer based supply chain strategy), and technology (for supporting the goals of the supply chain). Information Flow Tier 2 Supplier

Tier 2 Supplier

Customer

Manufacture

Customer/ End Customer

Logistics Sales

Purchasing

Finance

Production R&D Customer Relationship Marketing Customer Service Marketing Demand Management Order Fulfillment Manufacturing Flow Management Procurement

Product Development & Commercialization Returns

Fig. 1. Supply Chain Management (Lambert & Cooper, 1998) As late description, in next section a main framework will be represented to illustrate the impact of IT on SCM.

5. Definition of agility Agility is a business-wide capability that embraces organizational structures, information systems, logistics processes, and, in particular, mindsets. A key characteristic of an agile organization is flexibility.

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Initially, it was thought that the route to manufacturing flexibility was through automation to enable rapid change (i.e., reduced set-up times) and, thus, a greater responsiveness to changes in product mix or volume. Later, this idea of manufacturing flexibility was extended into the wider business context (Powell & Dent-Micallef, 1997) and the concept of agility as an organizational orientation was born. Agility should not be confused with leanness. Lean is about doing more with less. The term is often used in connection with lean manufacturing (Womack et al., 1990) to imply a “zero inventory” just-in-time approach. Paradoxically, many companies that have adopted lean manufacturing as a business practice are anything but agile in their supply chain. The car industry, in many ways, illustrates this conundrum. The origins of lean manufacturing can be traced to the Toyota Production System (TPS) (Ohno, 1988), with its focus on the reduction and elimination of waste. Provided that reaction of supply chain increased for responding the real demands, the agility of SCM grows. Emersion of IT and its application in SCM cause to virtual SCM emerges which is more information-based than inventory-based. So, collaboration along buyers, suppliers, and the firm enhances the agility of SCM.

6. The framework of leveraging the agility of SCM by embedding it The research revealed that the most impact of IT on SCM is on procurement, logistic, firm, vendor relationship management and CRM described in follows and illustrated in Fig. 2. The final and perhaps most important prerequisite is the need for a high level of “connectivity” between the firm and its strategic suppliers and customers. This implies not just the exchange of information on demand and inventory levels, but multiple, collaborative working relationships across the organizations at all levels. It is increasingly common today for companies to create supplier development teams that are crossfunctional and, as such, are intended to interface with the equivalent customer’s management team within the supplying organization (Lewis, 1995). Through using of IT in the supplier and customer area of SCM, the agility of SCM could be leveraged (Fasanghari et al., 2007, Fasanghari et al., 2008). 6.1 IT & procurement The use of the IT in managing purchasing in the supply chains has developed rapidly over the last 10 years. The research demonstrates that the IT is utilized in a variety of procurement applications including the communication with vendors, checking vendor price quotes, and making purchases from vendor catalogs. Vendor negotiation has also been streamlined through the use of the IT. Face-to-face negotiations are not used as frequently because the negotiations can conducted through the IT. This includes the bargaining, renegotiation, price, and term agreements (Olsen & Ellram, 1997). The receipt of queries from vendors, providing vendors with information, and the processing of returns and damaged goods were all handled by the IT. The other more popular use of the IT in supply chains is in order processing applications. The most frequent use of the IT here is in order placement and order status. Over half of the firms use the IT for this purpose. This has dramatically reduced the costs of order processing. The use of the IT in order processing has reduced the error rate involved in order processing. Errors now can be detected more easily and corrected more quickly.


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Fig. 2. Framework for impact of IT on SCM 6.2 IT & operation 1) One of the most costly aspects of supply chains is the management of inventory. The research has shown that the most popular use of the IT in this area is the communication of stock outs by customers to vendors, or the notification of stock outs by companies to their customers. The IT has enabled companies to more quickly institute EDI information programs with their customers. The IT has affected inventory management most dramatically in the ability of firms to be proactive in the management of inventory systems. This is demonstrated in the ability of firms to notify customers of order shipping delays and inventory emergencies, in order to decrease the delivery lead time and inventory. 2) Production scheduling has traditionally been the most difficult aspect of SCM. The IT has enabled firms to minimize the difficulty in their production scheduling by improving the communication between vendors, firms, and customers. The research showed that some of

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the firms in the study use the IT to coordinate their JIT programs with vendors. In addition, some of the firms are beginning to use the IT to coordinate their production schedules with their vendors. 6.3 IT & firm 1) To keep costs down, an organization must have a high level of discipline based on the size of the firm: each person knows what needs to be done, knows how to do it, and does it quickly and efficiently. To do this requires a discipline of change which encourages innovation, and yet retains the stability of existing procedures until innovations are ready for wide-spread adoption. IT could overcome this problem. 2) The need for continued learning is acute in today's competitive environment. As new teams are formed, individuals must be able to learn rapidly what is needed to deal with a new set of issues. As new knowledge is developed, it must be made available to other members of the team and to individuals in other parts of the larger organization, that IT has the main impact on improving this process. 3) An organization must be "tight" at the same time that it is "loose". By light, we mean the need to have a lean, disciplined operation, in which there is a strong and ceaseless attention to keeping costs down and providing quality service at the same time. By loose, we mean the need to be innovative, to be responsive to customers' needs, to be flexible and adaptive to changing conditions and changing customer needs in each local situation. This flexibility is the other area that IT has critical impact on firm in the SCM. 6.4 IT & logistic 1) The research showed that the monitoring of pickups at regional distribution centers by carriers is the most popular application of the IT in this area. This is particularly important for a company, since tracking shipments to regional depots provides the firm with data on the reliability performance of the carriers it is using. This enables transportation managers to make sure that the motor carriers they use are meeting their promised arrival times. 2) In production and logistics, many parties are involved in coordinating all the processes that are involved in fulfilling a customer's order: manufacturer, suppliers of parts and subassemblies, material managers, logistics managers, transportation carriers, customer service representatives, quality assurance staffs, and others. The goals are to reduce the cycle time to fill a customer's order, reduce the inventory of parts, work in process, and finished goods in the pipeline, increase the accuracy and completeness of filling a customer's order and of billing him for it, and accelerate the payment for the delivered items to put cash in the bank as soon as possible. To achieve this degree of Order Cycle Integration, manufacturers, merchandisers, and their trading partners are using IT. 6.5 IT & customer relationships Many management experts argue that, by focusing on total customer satisfaction, a company can improve its processes to deliver better service at a lower cost. Customersatisfaction driven is often described as the next step beyond TQM, total quality management: the objective is not simply to deliver some abstract definition of quality, but to deliver total satisfaction to the customer, of which the delivery of quality is only a part.


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Meanwhile, in the past, customer information could not be fully utilized in setting processes of firms’ conditions. With recent increase in the speed of the IT, it has provided firms with the ability to offer their customers another way to contact the firm regarding service issues and integrate customer information and firm information to bring great benefits to both customer and firm. The research shows that some of the companies use the IT to receive customer complaints, while the other utilizes it for emergency notifications. 6.6 IT & vendor relationships 1) For IT in general, Auramo et al. (2005) propose that IT deployment in supply chains leads to closer buyer-supplier relationships. Stump & Sriram (1997) provide empirical evidence that the use of IT is associated with the overall closeness of buyer-supplier relationships. Subramani (2004) reports a positive relationship between an IT-based supply chain and organizational benefits. Lewis (1995) suggest that the decision to use IT within the dyad could encourage the commitment to establishing relational behavior. Their results show that IT decreases transaction costs between buyers and suppliers and creates a more relational /cooperative governance structure. 2) Trust plays a key role in any organizational relationship that IT facilitates it. Trust exists when a party believes that its partner is reliable and benevolent (Heikkilä, 2002). There has been a noticeable increase in the importance of trust in different forms of interorganizational relationships in management literature. The need for trust between partners has been identified as an essential element of buyer-supplier relationships. 3) Studies recognize long-term orientation commitment as a predictor for successful interorganizational relationships (Bensaou & Anderson, 1999). Long-term orientation refers to parties’ willingness to exert effort in developing long-term relationships. Such willingness is frequently demonstrated by committing resources to the relationship, which may occur in the form of an organization’s time, money, facilities, etc. Productivity gains in the supply chains are possible when firms are willing to make transaction or relation-specific investments, an important indication of commitment that was increased by IT. 4) Several studies suggest that successful buyer-supplier relationships are associated with high levels of information sharing. Information sharing (quality and quantity) refers to the extent to which critical and proprietary information is communicated to one’s supply chain partner. IT caused to open and collaborative information sharing lead to positive effects on interfirm relationship.

7. Conclusion In this article, at first was presented the definition of IT and SCM and afterward the impact of IT on SCM was illustrated in a framework. It is important that, the impact of IT on SCM is much larger as it facilitates inter-organizational communication and in turn reduces cycle times and develops collaborative work. IT provides opportunities for an organization to expand their markets worldwide. IT opens up the communication and enlarges the networking opportunities. IT supports seamless integration of partnering firms. This facilitates an increase in agility and a reduction in cost. Also, IT enhanced teamwork and CRM for designing new products and receiving feedback from customers and being proactive on responding to change market requirements. Considering the recent trend in IT,

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more and more companies are attempting to use IT in producing and selling their products/services. Reduction of manual work and costs, improvement of information quality, speeding up of information transfer, and volume of transactions were found to be the drivers for the transaction processing role of IT in SCM. One set of strategies for gaining competitive advantage is based on a simple principle: use IT to enhance the ways in which people work. To improve the communication between customers and suppliers, IT would be useful in exchanging the information about products and services. Many companies lack knowledge and skills about IT. This could be due to lack of understanding of the implications of IT and lack of fund for IT investment. These require education and training and also government support to facilitate easy access to the Internet service and development of web site for use of IT in SCM. As a main deduction, IT is a major source to enhance the competitive advantages of the SCM. The implementation of IT in the SCM can enable a firm to develop and accumulate knowledge stores about its customers, suppliers, and market demands, which in turn influences firm performance. The key to survival in this changed condition is through agility in particular by the use of IT in the important segment of SCM. Moreover, the investment of IT for leveraging the agility of SCM can be optimized as the proposed framework for the affected dimensions of the SCM through IT support organizations for use of IT in SCM according to their goal and resources.

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11 Development and Evolution of the Tiancalli Project Macías Galindo Daniel, Vilariño Ayala Darnes and López y López Fabiola

Benemerita Universidad Autonoma de Puebla, Puebla, Mexico

1. Introduction Every year since 2003, a group of universities and research centers compete against each other to prove mechanisms for supply chain situations, in a simulation known as The Trading Agent Competition: Supply Chain Management Game (TAC SCM). Several common situations such as customer and supplier care, and storage management are considered as important but should always be well-balanced in order to obtain the highest amount amongst other five agents in competence. With the purpose of participating on the TAC SCM, the Tiancalli Project was created since 2004. Some experiments were conducted in order to develop the first agent –Tiancalli 2005- which participated in the current SCM competition, and since then, evolutions and changes over the structure of the agent have been done to create even better agents for fast decision-making. In this paper, we would like to present the course of this research, detailing it as much as possible. Thus a comparison between the three main versions of the Tiancalli agent, from 2005 to 2007 will be presented, in order to prove if the current agent plays better than the previous ones. In order to demonstrate the improvements obtained with every agent, an experiment with 100 games -25 for each version- has been designed and presented on the latter pages of this chapter. An analysis for each of the three agents is performed, by measuring the results obtained from each simulation. It is a fact that Tiancalli 2005 is more reactive than the 2007 version, which is presented as an intelligent agent. However, this fact will be discussed and even proved on this paper. This analysis will also serve to suggest new implementations for the new version of the agent, which will be participating on the 2009 game –due to the 2008 competition has already taken place. The metrics proposed and generated and the strategies and algorithms developed to make Tiancalli an agent capable of winning a higher amount of orders and a null generation of late deliveries are exposed. These both points are considered the most important on developing a successful SCM player agent.

2. The trading agent competition and the Tiancalli project Supply chain is the part of management which is “…concerned with planning and coordinating bidding, production, sourcing and procurement activities across the multiple organizations involved in the delivery of one or more products (Arunachalam & Sadeh,

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2004)”. With the purpose of analyzing the behaviour of dynamic markets based on supply chains, the Trading Agent Research Group has developed the TAC SCM platform. On this simulation there are three main entities: Customers, Suppliers and Agents, the latter also own an assembly factory and a bank account. Supply chains are the group of processes and activities required in order to transform raw materials to final products. On TAC SCM, a software agent should be developed by a participant institute. This agent should buy components, assemble them to create and sell computers. These activities are performed on a changing environment, against five other agents. The strategies implemented on each agent will allow it to gain customer orders and supplier orders. Finally, this will help the agent to get the highest amount through 220-days simulations. The use of agent technology is encouraged because of the following features: • Agents must be enabled to adapt themselves to their environment, and sometimes, with their actions, modify it. • Agents must be proactive; this means that depending on the see-through capacity of the agent, they must be able to modify “intelligently” their behaviour before an expected situation arrives. • Sometimes, agents must act as a “reaction” coming from the changes on the environment. • Finally, the agents must be able to cooperate and collaborate to offer accurate answers, in other words, to work as a society. 2.1 The trading agent competition: supply chain management game description The simulation consists on developing an autonomous agent capable of performing common activities on a supply chain process. The activities performed are, in general: • To attend a group of customers interested on acquiring a product, for this game it will be computers. The agent must propose them an affordable price that should be more interesting than the offers made by the rivals. • To request a group of suppliers to obtain the raw materials to assemble a computer. For this game, the components to produce a PC are: processor, motherboard, memory and hard disk. The agent should be able to request the materials and offer an interesting price to the producers –better than prices offered by the rivals-, in order to acquire components. • To organize and plan the production and delivery of computers on time to avoid penalties for late deliveries or for storing too many components or computers. • All the entities of the game are limited in resources: the suppliers have a limited production, and the agent owns a factory that can produce less than 500 computers per day. The participants are then competing against them to dominate the market. • The market is not a static environment, so the agent must be able to adapt to the current conditions, for example: unavailability of components, preference over certain type of computer, and other conditions which may be changed by the other agents on the competition. The TAC SCM scenario is formed by six agents capable of producing PCs, a group of customers and eight different suppliers, two for each type of component. The simulation longs 55 minutes that represent 220 TAC days when the agents have to make important decisions about their tasks, in order to obtain the highest incomes and become the winner.

Development and Evolution of the Tiancalli Project

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The agent starts without money in its bank account. The common competition is to go from negative amount to positive during the whole game. Then, it is important to obtain components, sell them and avoid the penalties that can be applied for both late deliveries and component storage. The components available are listed as follows: (a) Processors of 2 or 5 GHz, supplied by Pintel or IMD –so the processors are the only element that can be considered as four different; (b) Motherboard for Pintel or IMD processors, supplied by Basus and Macrostar; (c) Memory of 1 or 2 GB, supplied by MEC and Queenmax; and (d) Hard disks of 300 or 500 GB, supplied by Mintor and Watergate. The total combination gives 16 types of computers and 10 different components to combine. This is a brief competition of the TAC SCM game. For more details about it, please check (Arunachalam et al, 2003). As it is mentioned, many universities and research centres have tried to approach the problem from different perspectives. Some of the most remarked techniques include fuzzy logic, stochastic methods, regression trees and prediction techniques, all of them applied on Multi-Agent and Sub-Agent systems. With the purpose of creating an agent that could participate on the TAC SCM tournament, and following a philosophy of gradual improvements by knowing the basics of the competition, the Project TIANCALLI was created on 2004. The project consisted on presenting an agent for the following SCM games, by a process of reading, comprehension and implementation of several learning techniques. Several experiments which allowed understanding the behaviour of the simulated markets were run before obtaining the first agent, Tiancalli 2005. There were other implementations on the following years -2006 and 2007-, and we postponed the presentation of the 2008 agent because of lack of time, for 2009. With each year, the agent presented many improvements, especially on areas such as: • The relationship with the customers. • The relationship with the suppliers. • The use of the full capacity of the factory during the whole simulation. • The decisions taken in order to obtain more incomes than expenses. In this chapter, the full notes of each of the performed Tiancalli agents are presented. It is organized as follows: On section 3, the nine non-so-called-Tiancalli agents are presented as a background of the first analysis made about the TAC SCM game. On section 4, the “Tiancalli 2005” agent and its features are discussed. Then the sections 5 and 6 present the background and the final implementations for the agents “Tiancalli06” and “Tiancalli07”, where a subagent perspective was approached. On section 7, a mathematical comparison between the obtained results for these three agents is presented and discussed. Finally, section 8 and the conclusions present the issues not corrected on the previous versions of the agent in order to obtain the principles to construct the Tiancalli09 agent.

3. The agents before Tiancalli In order to know how to approach to the SCM game, several experiments were conducted to know how the platform functioned. These agents served to know which strategy could work better with the found limitations of our first design, this is why they are mainly divided on two groups: • Those agents which purchase components after the orders from the customers arrive, which are known as the “Loco_Avorazado” agents –which can be translated as “Mad

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and voracious”. They were implemented by considering the features of the voracious algorithms. They can be easily described as agents who want to “have customers first, and then find how to satisfy them”. • Those agents which purchase components before the orders from the customers arrive. They are better known as the “Previsor” agents –which can be translated as “Foreseer”. Their philosophy is based on the idea of “selling what we already have”. Both approaches served to know how the platform behaved. These agents used essentialy the same commands but allowed to understand how much does a supplier delayed on delivering the items to the agent, and how to control the storage and the assembly factory of the agent. 3.1 Loco_Avorazado agents A. Loco_Avorazado agent. Two versions of these agents were constructed. The first version had implemented the following behaviour: • The agent selects its customers by taking the due date of each request. This date must be of eight days after the current TAC day. • The agent offers to customers at 95% of the customer suggested price. • The agent requests its components after the offers to the customers have been made. • The agent chooses its suppliers at random. The results obtained for this agent are shown in Figure 1. Some observations were made and are described next. • The agent takes 10% of total requests sent in a TAC day. • The factory of the agent is used in a 24%. • The customer chooses this agent in 96% of the offers made by it. • The agent delivers an average of 80% of the total orders obtained. • The incomes for this agent are between $4 and $8.2 M, and it always obtains the last place. B. Loco_Avorazado_2 agent. The second version, named Loco_Avorazado_2, was implemented long after the first version. It implemented several features of the Previsor agents –which will be described next- and it served to discard or continue implementing these agents. Its behaviour can be described on the following quotes: • Considering the delivery problem of the previous version, this agent chooses customers by taking the delivery date of the request of over 10 days after the current date. • The agent offers to the customers a static price of 95% of their suggested price. • The agent also makes requests of components in the same day that the offers for customers are sent. • The agent chooses suppliers at random. It means that the preference concept is not implemented here. After implementing the agent and obtaining results, some other facts obtained of the performance of this agent are discussed next. • Loco_Avorazado_2 takes almost the 25% of total requests made daily by customers, and gains all these requests in a static price environment. The agent uses over 60% of factory duty cycles. • • The customers prefer the agent offers in 96% of the cases. • The agent has incomes from $4 to $37 M and can reach first place in most of the simulations.


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3.2 Previsor agents A. Previsor agent. The agents that belong to the Previsor Series implement the strategy of “first buy, and then sell what was bought”. The first version of this agent works as follows: • The method for handling customer requests chooses them by defining a function value that considers the suggested price and the quantity of computers that the customer wants to purchase. This function is established on (1):

f =

reserve Pr ice + (quantity *100) 2

(1)

Where: reservePrice is the price suggested by the customer (top price) quantity is the amount of computers requested. All those request that exceed a f value of 1500 are considered to be attended by the agent. • The assembly of computers is made when the offers are sent by the agent to the customer. • The agent obtains its inventory a priori based on static daily amounts. Then it has to optimally assign the inventory and the free duty cycles of the factory. When the inventory and the free cycles are calculated for the current day, an optimization problem is organized and intended to be solved. This problem is formulated on (2).

Maximize n1 x1 + n2 x2 + n3 x3 + ... + n j x j adjusted to :

k1 x1 + k2 x2 + k3 x3 + ... + k j x j ≤ 2000 c11 x1 + c21 x2 + c31 x3 + ... + c1j x j ≤ 100 c12 x1 + c22 x2 + c32 x3 + ... + c 2j x j ≤ 100

(2)

... c1i x1 + c2i x2 + c3i x3 + ... + c ij x j ≤ 100 Where: xj can be binary {0,1} j is the amount of requests that satisfy the conditions to be considered n is the product of the quantity and the unit price of the request k is the product of the quantity and duty cycles c ij is the quantity of component i used in request j This problem is best known in literature as the Multidimensional Knapsack Problem. It can be solved by using different types of algorithms. For this strategy, we solved the problem using a Monte Carlo Algorithm. This algorithm randomly selects the requests that could be satisfied with agent limitations. With this mechanism as the essential core of this and the following Previsor agents, this version achieved the following results: • The agent has a customer acceptance rate of 96%, mainly because the agent offered the 95% of the price suggested by the customer. • Monte Carlo Algorithm is a selection method that is little effective for so many request in a very few seconds. Some days, the algorithm never returns a solution, so the agent cannot take any request. Then, the agent loses a lot of money because of the a priori purchasing system.

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There is an important stock of components and assembled computers at the end of the round, basically because the objective is to always have items on the store. Then a mechanism for managing the inventory is required • The Previsor agent always makes 6th on any round. B. Previsor_2 agent. This agent is an improvement of the Previsor agent. It was intended to correct the absence of response by using the Monte Carlo algorithm, by implementing a selective algorithm. Its functionalities are described next. • The agent chooses its customers by using the same function than Previsor as the first filter to choose customers. • The agent obtains its inventory by purchasing a constant amount of each component on each TAC day. • Its customer requests selection works with a selective algorithm instead of the Monte Carlo algorithm of Previsor agent. In this case the agent orders upwards the requests by considering the f value of the first filter, and decides which ones could be satisfied with both the inventory and the free duty cycles limitations. • The orders for assembling computers are made once the offers are sent to the customer. • The results of one simulation with this agent are shown in Figure 3. Some remarks on this agent are shown next. • The customer accepts 99% of the offers made by the agent. So, a feared trouble about the inventory is partially corrected on a static environment –because the Dummy agents do not change their prices. • The factory of the agent has a remaining inventory –but quite lower than the Previsor agent stock. • The incomes for this agent are from under zero to $14 M, and sometimes it gets from 3rd to last place. This lack of consistence in the results is not enough to win to a Dummy agent. C. Previsor_3 agent. This agent became the first winner agent of this project, working over static environments with Dummy agents. Its features are enlisted next. • The Previsor_3 agent uses the same function to handle customer orders. However, it implements a new component purchase systems that defines certain quotes for purchasing. These quotes are established as follows: A Normal Quote is applied when there are between 500 and 2000 components on stock. An Extended Quote is applied when there are less than 500 components in stock. A Minimal Quote is applied when there are more than 2000 components in stock. These quotes are applied for each TAC day, excepting the last 10 days when the agent applies only Minimal Quotes. There is also an important rule about the quotes. The quotes for purchasing processors are always half the quotes for purchasing motherboards, memories and hard disks, because there are four types of processors and only the two types of the other three components. The objective of this rule is to have less stock at the end of the round. • Computer production is made in the same way than every previous Previsor agent. • The agent limits its components purchase. This is made in two ways: the agent offers computers until three days before the game ends. Also, the agent considers all the customer requests before the game ends. This is done in order to empty the inventory. • The results of Previsor_3 agent in a game are shown in Figure 4. Other results are discussed next.


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The agent offers prices in two ways. They could be static at 90.7% or non-static in a random range between 75 and 90.7%. The agent always wins the customer orders, in a static pricing environment. • There is a remaining inventory of components and computers, but lower than on Previsor_2 agent. • The agent gets incomes from $29 to $40 M in the rounds. This performance makes that agent to get the first place in all simulations competing against Dummy agents. • All orders received by the agent are delivered on time; there are very few penalties which do not interfere with the final result in all tests. D. Previsor_4 agent. This was the latest Previsor implementation, which allowed starting with the development of the first “Tiancalli 2005” agent. Its features are presented next: • Its behavior is similar to Previsor_3 agent except that this agent has methods to sell the assembled computers in stock to other customers; it means, in case that a customer does not accept the agent offer, the assigned computers for this sale are reassigned to other requests, and so until they are out of the stock. This was implemented after developing a prototype of Vendedor_Inmediato agent, a hybrid that will be discussed next. • The agent also offers non-static prices, depending on the behaviours of the other agents in the current game. As this was the latest agent of the platform research phase, it was intended to give this agent the chance to change the prices given to the customer. This was done by considering the acceptance and reject of the offers sent by the agent, and the price was moved from 75 to 95% of the customer suggested price. • The results of the performance of the Previsor_4/4x agent are shown in Figure 6. Some other interesting facts of this agent are shown below. • This is the first agent that implements a system of non-static prices which is not random –as on the hybrid agents shown next. • Almost all deliveries are on time. There are very few penalties which do not affect the final result in all tests. • There is an inventory remaining of components and computers. It is the smallest of any other implemented agent at this point of the work. • Previsor_4 gets incomes from $19 to $30 M in test rounds against Dummy agents. • This is the first agent that was tested to compete against the other ones implemented in this work, and always reaches first place. 3.3 Other agents Some other hybrid agents were created, in order to test other possibilities for analyzing the platform. They are described below. A. Vendedor_Inmediato agent. This agent implements the same function for the suggested price and computers quantity that Previsor series. Also it is a testing agent for computer inventory handling, which leads to the development of Previsor_4/4x agent (described previously). This agent purchases the components a priori. When the components are delivered, it orders the factory a constant number of computers that must be assembled. Then the restrictions for this agent are not the components nor duty cycles, but the assembled computers currently in disposal. This agent could have been the best agent in the phase, but due to the lack of time to develop it, it was left as a reference intend for the agents developed on the following years.

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It was a good step to avoid the dependence on the factory cycles, but the main situation was that a higher penalty is applied for computers than for components. So in a real competition, to risk this situation is very difficult. B. Previsor_Avorazado agent. It was a combination of both Previsor and Loco_Avorazado strategies. It takes the early purchase system implemented in Previsor agent and its filters to consider requests and assembling, and the ability to search for more incomes with the due date mechanism of the Loco_Avorazado agent. • The agent chooses customers limited by components in store and duty cycles. It also chooses extra customers that their due date is over 10 days, both of them must previously satisfy function value. • The offers made by the agent have static price about 90.7% of the suggested price for customers –this value was considered from previous agents. • As the agent chooses the selected requests, an extra amount of no more than 10% of the total production is sent to the “long-term production”. This means that the agent should ask for these extra components and try to assemble these computers on the following five days at least. However, as the factory is mostly used at full capacity, it was not clearly defined how to obtain this daily 10 percent required for extra orders. It was mostly obtained from rejected offers. The performance of Previsor_Avorazado agent in a game is shown in Figure 9. Some other observations are made below. • The agent gets all the orders from the customers, because of its static prices of 90.7%, which can not be improved by a Dummy agent. • The factory uses over 60% of its daily duty cycles. • Some orders which should wait for components are sent before the components are received. Then some orders are delivered late or never delivered, or the buy ratio is smaller. • There are more outcomes than incomes. However, in some simulations, the agent gets first place and in the worst case, the 4th place.

4. The Tiancalli 2005 agent The presented background is quite consistent to provide tools to develop the first version of the “Tiancalli 2005” agent. As it was explained before, the agent is based on the implementation of Previsor_4x agent, but it was modified to consider the market price changes which occur through the competition. As a first effort to get a well-defined structure of the agent, it was partitioned on three submodules, which persisted over the design of the following versions of the agent. These modules are explained next. A. Customer Selection System (CSS). The submodule included all the behaviors to choose and give a price to an expected customer. To choose a customer, the agent has two established filters. On the entire incoming request, first the agent has to consider that the customer must offer a higher amount than the production cost. This cost is calculated with the function seen on (1) where, as previously seen, reservePrice is the customer suggested price, and quantity is the amount of computers requested by the customer. The f parameter is a numerical value which was determined through several calculations as the average cost for any computer – not exactly the real one, however. Thus, if the request exceeded an f value of 1500, it is considered as winnable, which means that the agent may get an income for delivering this


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order in time. Then the request passes to the second filter. All the approved requests are ordered from top to down considering the reservePrice offered. The second filter is applied once the Inventory System has determined the current inventory. Then, the entire ordered requests are subject to the availability of components in stock. If there is enough stock to satisfy the complete order, either by: • using already-assembled computers -and if that decision could not affect another previously made orders; or, • using the stock components –which will require duty cycles from the factory. Those requests that pass the second filter are then considered as offers, and the next step is to define the price for the customer. However, to use already-assembled computers, the agent must have considered this order delayed, in other words, that the customer is no more expecting the requested computers. In most of the cases, the agent only offers computers that still should be assembled. As the offered price has to be at least equal or less to the customer suggested price, this price can be calculated in two different ways: a) Mirror Pricing. When the reservePrice is higher than the base price defined by the agent during the simulation, Tiancalli uses the Mirror Pricing Strategy. It consists on applying the function (3) to the reservePrice defined previously, as shown. reserve Pr ice ⎞ ⎛ off Pr ice = ⎜ 2 − ⎟ * reserve Pr ice base Pr ice ⎠ ⎝

(3)

The basePrice applied is the static price of a computer by considering the initial price of the components required to assemble it. Commonly, the obtained offPrice will give a lower price than the customer suggested price. Otherwise, then the next strategy is used. b) Factor Discount. In the special cases where the reservePrice is smaller than the base price, the Mirror Pricing strategy could give a higher price than the customer price. Then another strategy should be applied. A factor discount for each type of computer –which is stored in the Inventory System- is taken and applied to the price obtained with the previous strategy. It is certain that always the price will be lower than the original customer price, however if it fails, the Inventory System can apply another discount –this will be discussed on next. The function used then in this strategy is represented on (4):

offPrice suggestedPrice discountFactor

(4)

This discount factor is modified for each computer type at the end of each TAC day, using the following algorithm: For each computer i[1...16] and this i computer discountFactor df[0...1] Calculate Factor = orders received / offers sent If Factor value goes: under 30%: lower df by 5% between 30 and 90%: lower df by 1% over 90%: wait for 5 days with the same df value; if the condition is maintained, increase df by 1%. Next i

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The orders received come from the offers made on the previous TAC day –so the offers must be saved on the agent IS. If just a few orders –or none- have been received with this discount factor, it means the offered price is too high, so it must be highly decreased. If most of the offers become orders, a trigger is thrown in order to expect good results with this price and then, if this condition is maintained on the following four days, the discount factor is increased. There is an additional strategy that is normally applied after the TAC day 200. It is applied to decrease the amount of components in stock. However, this strategy can be applied if the agent has not gained any order on five straight TAC days. With this strategy, the agent fixes the discount factor under 50%, as an emergency method to gain customers. B. Supplier Purchase System (SPS). The platform had a bug which allowed agents to make big purchases at the zero day of the competition –the configuration day- so as it was unknown to us once the competition started, a very primitive strategy was implemented, in order to get components for five non-consecutive TAC days. After the “zero day”, and through the first ten TAC days, Tiancalli tries to purchase components for making an initial stock. So the agent does not offer computers until TAC day 11. Then the purchase of components becomes somehow difficult and limited because of the limited production due to the zero day bug. To calculate an accurate price for the supplier, a similar idea to the customer pricing was implemented. In this case, instead of a percentage, a binary parameter was used –if the request became an offer from the supplier or not. On the first case, a trigger to wait if the price is accepted on the following three TAC days is thrown; then the offered price may go down a percent unit. On the latter case, the factor for the component must be increased 2% and expect the next day result. As it is shown, the agent only makes one request per day for each component. This was procured in order to maintain certain organization with the requests; however it is insufficient to reach the half of the game with many components in stock to keep production levels high. The SPS communicates with the IS as it holds the component prices and the pricing factor committed previously. Depending on the level of stock maintained for each component, the agent may request a different amount of components, which is explained next, as the quota system was explained before in the paper: • A Normal Quote requires 100 components. • An Extended Quote requires 250 components. • A Minimal Quote requires 10 components –this quote is commonly used to poll the market. The proposed quota must be modified as each TAC day passes, for example: a Normal Quote applies for 500 to 2000 components on the early competition days. However, the quote is reduced on 10 to 100 as the simulation comes close to the end –approx. on day 200. C. Inventory System (IS). The IS works more as a database than as a system, because it stores most of the factors, prices and numerical considerations applied on the performance of the agent. This system offers limits for prices –for example, no discount factor for computers may go under 40%, however it is a possible situation for any game. The IS is modified by the methods applied within both CSS and SPS, considering the customer acceptance ratio –the amount of orders against the one of offers sent by the agent-, and the supplier acceptance value –if it was accepted or rejected.


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First the Tiancalli agent was able to choose a discount factor of just one value for all the computers and another for all the components. It was necessary to implement an array of sixteen values for each computer and ten for each component. These arrays -which represent discount factor for each component and computer-, were used to give the agent an individual perspective of each market element during the simulation. D. Issues related to the performance of Tiancalli 2005. The Tiancalli 2005 agent had a desirable performance, by achieving the mid-table during most of the competition and reaching the Quarter Finals. The main issues found with this agent are enlisted next: • The final implementation of the agent was created during the competition, so many modules and methods which were programmed on the agent do the same. • The methods for modifying the discount factors are very restrictive and are commonly modified during a competition. The agent goes from 95% to 40% and resets to 95% in no more than twenty days, so the prices do not remain static in most of the time. • The agent does not take automatically any learning from each game, as the values are reset on each simulation, and if some learning is acquired, it is applied by the programmer and not the agent itself. • The simulation was not completely understood, and several facts, such as the factory usage, the punctual deliveries and the disposition of components, did not work properly, delivering several penalties to the final results of the agent in each game. These are some of the issues that are expected to be corrected on the following version for 2006. However, for a better comprehension and review of the previous agents and the performance of Tiancalli 2005, we encourage you to read (Macías et al, 2005).

5. Development of the agent Tiancalli06 Tiancalli06 is a software agent that develops real-time techniques such as case-event learning. This allows the agent to improve to the Tiancalli 2005 agent on the following tasks: • It has an improved mechanism for managing the component purchase, in order to become the preferred customer to the suppliers. The agent offers a better price based on the current situation of the game, in order to dispose of components during the whole game. • The price selector for customers is improved by considering several other factors such as the current day and acceptance in the market for each computer. • The factory is better organized and the agent intends to keep the lowest stock possible to satisfy orders on the following days. This agent is organized on a better architecture which has the same three modules –defined from now on as sub-systems- of the previous agent, but well defined and separated. Then, if a modification is required, the programmer may direct easily to the required class and update it without affecting the normal performance of the agent. There are other classes included on the architecture of the agent that keep the game information inside the agent, and allow displaying the relevant information of a game on output files. The architecture is displayed on Figure 1. A. The Customer Selection System version 2(CSSv2). For the analysis of the customer requests, the agent considers and makes the following activities: • At the beginning of the game, the agent applies an initial discount factor for each type of computer –this discount factor works as in the previous version.

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Fig. 1. The architecture of the agent Tiancalli06.

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From the TAC day 11 and until day 216, the agent analyzes the customer requests –the final day was decided because this day is the latest when the agent could delivery an order because of the process that will be explained later. The analysis of requests is done considering the available inventory for the next TAC day and if the request may produce higher incomes. The latter is determined by comparing the offered price with a calculated average price, which is described on function 5.

csp × dsf pcType ≥ mp pcType

(5)

Where csp is the price that the customer is willing to pay; dsf is the discount that the agent offers to the customer for this PC type (pcType goes from 1 to 16); and mp is the lowest price for a computer determined by the agent. This value is calculated by adding the average price of the four components that form any computer. Hence the first filter considered for the previous agent was changed for this process on this version. The entire group of requests that satisfy this requirement will pass to the following filter. • The second filter works as on the previous version, any request for there is either enough both components and duty cycles, or enough free computers in stock, will be considered as offers. The determined price for these offers will be these which were calculated on the first filter –then, to save time the agent saves the price when the first filter is passed so when the offer is sent, it is sent with the stored price. • The discount factor can never be more than 100% -in fact, it is never over 95% for most of the games-, or less than 42%. This is done in order to offer an optimal price to the customer that guarantees an even little income. • The mechanism for changing the discount factor was slightly changed from the previous version. The calculation is the same; however more situations to move the prices are different, with this a better movement of the prices is allowed, and the agent


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may even try with high prices for the customer. Then the function to determine the discount movement is shown next. factor = (orders from the current day) / (offers from the previous day) If Factor is: - Zero, then to lower the discount by 10% - under 20%, then to lower the discount by 2% - between 20 and 50%, then lower the discount by 1% - between 75 and 90%, then keep the discount - over 90%, then increase the discount by 1%.

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The complete routine to deliver a PC to a customer consists on three TAC days, as following: DAY d: The agent receives the request and sends an offer. Then the agent plans the factory usage for the following day –if required, only if the computers are not still assembled. This behaviour is approximated to the one implemented on the Previsor agents; however the computers are assembled before the agent receives any response from the customer. DAY d+1: The agent receives the orders from the customers; if not, the rejected computers will be, on the following day, disposed for other requests. If the orders are received, the agent plans the delivery of the computers for the following day –because the computers are being assembled on the current day. DAY d+2: The request is satisfied, and the agent expects the incomes on the following days –for the agreed date. With this new structure of the customer attention, the agent is pretended to deliver on time all of its orders. It is expected that the agent increases its acceptance amongst the other agents, by evaluating simpler parameters for giving prices to the requests received. B. Supplier Purchase System version 2 (SPSv2). For the interaction with the suppliers, the second version of this agent implemented the following activities: • An improved purchase for the zero day. With this, the agent gets approximately the 35% of the total purchase of the game on the first day. The agent requests the components on determined days which are considered as crucial. The days are 9, 25, 50 and 100. • The pricing for the suppliers is proposed on the same way that the previous version of the agent, with a specific discount factor for each type of component –not so for each seller. Each TAC day, the agent requests no more than 100 components to each supplier. • The price is influenced by the acceptance or rejection of the price of the previous days, and each day that the price is accepted, the system receives and stores this price and calculates an average with the previous accepted prices, in order to propose the computer base price that is used on the CSSv2 system. Then the discount factor movement is determined with the following pseudo code: If request is accepted then decrease discountFactor by 1% Else increase discountFactor by 1.5%

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The system pretends to offer an initial purchase volume of approximately 70,000 computers per game –this was a calculation employed to determine how many computers an agent can assemble using the total factory cycles and components. Obviously this quantity can be affected by the market preference –maybe one type of computer is more purchased than the other-, so the system must be able to determine when a computer is preferred, and intend to offer more of these computers. Then, the system fixes at the start of a game, an initial quote of 70,000 components. When this quantity is almost to be reached and there are more TAC days following, this quote is modified as follows:

for id = 1 to 16, do: if (initialQuote (componentid) * 0.9) • soldComponents(id)) then initialQuote(componentid) is increased by 500 components

•

It must be noticed that the count that matters is the quantity of components sold and not those purchased. This feature is intended to decrease the amount of stored components at the end of the game. C. Inventory System version 2 (ISv2). On the new version of the IS, a more precise calculation for stored components and computers is obtained daily. Before deciding if most components must be purchased, the real quantity available of components available in stock is determined with the function (6). realInventoryForNextDay = componentAcquiredYesterday − componentSoldYesterday

(6)

This formula marks a significant difference against the agent of 2005, because it only considered raw components that were purchased and not those which are still waiting to be assembled. Also, this version considers computers in stock that have not been sold. The agent has on disposal such components –for they need duty cycles to become computersand computers to be offered to the clients. However, this system is centralized and offers the inventory and the calculations to perform the operations of the other two subsystems. D. The performance of Tiancalli06. The main issue that Tiancalli06 deals with is the price estimation. The changing conditions of the market and the changes that several agents try to impose on the competition are the most difficult challenges for a very sensitive mechanism of pricing. However, the performance of the agent was acceptable, especially on the Seeding phase of the competition. The agent achieved a constant acquisition of orders through most of the games; however the agent had difficulties in most of the games to get orders due to this pricing mechanism. Anyway, the most important achievement of the agent was the avoidance of late deliveries. The agent deliveries all of its orders in time, and just one game had penalties because of a connection issue at the end of the game. Finally, the storing prices were reduced because of the new quotes mechanism for purchasing components that were described on the SPSv2. For second year, the agent lasted until Quarter Finals and achieved a final 18th place. This year, the competition presented more agents and the improved versions of the previous participants. Although the advances were significant, the next version of the agent should offer better pricing systems. Some of the other agents implemented fuzzy logic or prediction heuristics to determine the prices. Then the next effort for Tiancalli07 should include an intelligent mechanism for pricing. The results obtained with the agent can be consulted on (Macías et


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al, 2006, 2) and an interesting comparison about the results of both agents Tiancalli 2005 and Tiancalli06 can be found on (Macías et al, 2006, 1).

6. Development of the Tiancalli07 agent In the effort of improving the performance on the activities that the agent does on the competition, the agent Tiancalli07 was developed. This agent features new prediction techniques to give prices to both customers and suppliers. The obtained prices are stored in external files, which are updated during the current game, and used for the upcoming games. Since the beginning of the participation of the Tiancalli agents, any of the developed agents took experiences from the previous games and stored the information automatically, so it is the first agent which can be considered as “evolutionary”. As it is the last agent developed by the same team project, it should offer a brand new and detailed architecture, conformed by three subagents –as defined on (He et al, 2006)- with specific functionalities, and two extra modules for information control. The general architecture is presented on the following figure.

Fig. 2. Architecture of the Tiancalli07 agent. A. Customer Selection Subagent version 3 (CSSv3). The general problem of attending the customers is subdivided in several areas. This allows the recognition of the main activities in order to correct any significant behavior. Some of these areas are described with their current developed activities: • Customer selection. This is the least modified area of the general problem. It uses both filters applied since the CSSv2 on Tiancalli06. Just as a reminder, the first filter checks all those customer requests for which their customer suggested price –multiplied by the current discount ratio- satisfy the base price for assembling the current computer type. The second filter checks the availability of both components and duty cycles, or free computers that remain in stock. Those orders that satisfy both filters are considered as reasonable and the subagent then intends to give them a good price. • Customer pricing. This are, on the contrary, is the most modified of the agent. A regression tree has been designed to have a better statistic of pricing movement. The tree is controlled by three main variables, which are described as follows: Requested quantity of computers. It is required to order each type of computer, from top to down, as all the requests have arrived to the agent during the past

208


-

days. For the classification, fuzzy values of maximum, high, medium and low are applied to determine this variable. Price ratio. This is determined by considering three prices: pmin which is the minimal price that offered the agent on the previous day and became order; pens that is the base price to assemble the requested type of computer; and psug that is the suggested price by the customer. However only three cases are considered – because the others are illogical: psug > pens > pmin → case 1 pens > psug > pmin → case 2 pens > pmin > psug → case 3

-

Range of days. It considers the day when the computer is requested. The whole competition is divided then in subintervals of ten days, which give a total of 22 different ranges. Once the concepts for pricing are presented, the structure of the trees is suggested. Sixteen trees, which represent each type of computer, were built to represent the required structure. Each tree has four branches for the requested quantity of computers, then three subbranches for price ratio, and finally 22 leaves for each sub-branch. This leaves a total of 243 leaves, which include each a range of values –maximum and minimum- that is used to determine the customer price. The leaves also include the acceptance ratio of each leaf during the current game. The acceptance ratio modifies the range of values as following: • If the prices are accepted, the tendency is to close the intervals in order to find a convergence on the prices. If the optimal is found, the optimal should tend to increase its value in order to search for more incomes. • If the prices are rejected, the tendency is to open the intervals, in order to reduce the minimal price and improve the prices against the other agents in the competition. The trees are updated with each TAC game and stored on external files. These files are loaded at the zero day. B. Supplier Purchasing Subagent version 3 (SPSv3). The subagent is in charge of performing the following activities: • Price calculations. In order to calculate the base price that is considered on the first filter of the CSSv3, this subagent must determine a price for each component. This is done by applying the following pseudo code: Variables: Input: p as the offered Price, q as the quantity offered Intermediate: qPurchased as the previously purchased amount of components, avgP as the previous average price, temp as a temporary variable. AT THE BEGINNING OF THE SIMULATION: qPurchased Åq, avgP Å p EVERY DAY WHEN THE AGENT RECEIVES A SUPPLIER OFFER: temp Å qPurchased + q


avgp ←

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qPurchased × p q × p + temp temp

qPurchased Åtemp This calculation generates a base price for each component, which once it is added to the remaining components, can be applied for the base price of each computer. This price is better approached than the previous versions, because the price is determined by the amount of computers which have been already acquired. • Component purchasing. The subagent recognizes two ways to purchase components: one at the beginning of the game with the zero day purchase, and the other during the competition. The first purchase is intended to get an initial stock for the first orders; the latter purchase is to maintain this stock. As the zero day purchase was corrected by the game developers –you can still buy components but the prices are higher and consider a general statistic for all the requester agents- the amount of components acquired on these days has been reduced; hence the strategy must be improved to acquire the items during the whole competition. The pricing system implements a single list for each component –ten lists in total- with 22 spaces. These spaces should be filled with the maximum and minimum ranges of discount factor during each 10 TAC days. As it can be seen the structure is similar but simpler than the trees implemented for the customer pricing. The ranges are modified by following these rules: • If the supplier accepts the discount, the ranges are closed, tending to find an optimal. Once an optimal value is found, decrease this value to find a minimum value. • If the supplier rejects the price, the ranges are opened by increasing the maximum price, in order to improve the prices and make them competitive. C. Inventory Subagent version 3 (ISv3). The ISv3 is in charge of organizing production and delivery of computers to the customer. Its main goals are the following: • Avoid the loss of orders by production delays, and reduce at most the delayed deliveries, if possible nullify them. • Bring an accurate use of the factory and stock of components and computers, by delivering real statistics of the current situation to the other subagents. Sometimes this statistic must foresee the availability of components that will arrive on the following days. D. Support packages. The whole operational environment of the Tiancalli07 agent includes two support packages that will be described next. • Information Access and Configuration Package (IA&CP). This package is in charge of handling the most important data about the current simulation, in order to bring these data fast to any requiring entity. Also several methods for reading and writing external files are included to save the knowledge generated during the simulation. Finally, a configuration file is included, which commands the whole system the files that will be used to generate the initial trees and lists for the current simulation. The configuration file and the files for the storage of the structures are overwritten or substituted at the end of a simulation. • Support Interfaces Package (SIP). The current package is only used for developing purposes. It implements classes for graphic interfaces to manage the information generated during a game. The programmers can so recognize the behavior of the current simulation with more detail than with the sole Agentware interface –the

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original SCM interface included with the downloadable test agent. The developed interfaces are to display information such as: (a) maximal and minimal customer acceptance prices; (b) amount of requests against amount of orders, and (c) demand representation for the customers. E. Implementation of the knowledge repositories. The files for configuring the agent are stored on a subfolder named “playbooks”. The first read configuration file is named “init.tcf” and includes, mainly, the names of the files that include both the trees –p####.cus, where # is a serial number- and the lists –s####.sup. The serial number is updated once a simulation is finished. The general structure of a customer tree file is the following: p0003 //file name 16 //number of trees included 4 //possible values for the first parameter of the tree 3 // possible values for the second parameter of the tree 22 // possible values for the third parameter of the tree a0a 90000 100000 10 19 a0b 90000 100000 15 16 a0c 90000 100000 24 30 … The first five lines of the file are explained in the structure itself. The following lines are the ones that represent the tree structure as following: the first letter (a) could have three other values (b, c or d) and represents the demand of the current computer type –where a is maximum and d is low, as explained before-; then a number (0) can take other two values (1 or 2) and represents the price relationship; and the last letter (a) represents the segment of time on the competition where the computer is requested –it may have 22 values from a to v, where a is the segment from day 1 to day 10 and v the segment from day 211 to 220. The following two quantities -90,000 and 100,000- represent the percentage applied for the discount factor. They are not decimal values because of the expense to store a double value, and the capacity to represent exact quantities which can not be represented with a wide double variable. This is explained as follows: Considering the minimum amount that can be applied to the percentage discount so that you can discount one unit of money, the obtained number is 0.00012. So if for example, the range is between 90,000 and 90,012, the price may vary in just one unit, maybe the price will be 1500 or 1501 with this range. The value of 0.00012 is considered as the less significant number in the price calculation. This is why an integer –formed with the double value and multiplied by one hundred thousand- is required to store this factor. The last couple of numbers represent the ratio of orders against the offers proposed. So in the first element of the tree –a0a- it can be observed that this leaf has generated 10 orders of 19 offers to the customer. Then the efficiency of the leaf can be also obtained. This structure of the file and the tree allows the system to obtain easily the leaves, so the time required to seek the leaf is minimum. But the structure to store the prices for suppliers is not so different. The list is stored on a file named “s###.sup”. Here an example is presented: s0003 1 16 22


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aa 74450 74453 11 13 ab 69550 72864 8 10 The file stores the name of it, which is s0003. It will propose the construction of only one list of 16 x 22 elements (the sixteen components divided by supplier and the 22 ranges of TAC days, as explained previously. The list is represented now with two elements –aa, ab- which represent, respectively, the component type and the range of days. F. The performance of the Tiancalli07 agent. The results obtained with this agent were compared with the previous two agents by separate on (Macías et al, 2007). However, the results in the competition were not as good as expected: the agent achieved again the 17th place. The comments about this result and several remarks on what issues should be corrected on the next version of the agent are discussed on the conclusions of this paper.

7. Experiment In the previous lines, all the Tiancalli agents that have been developed until now have been described with detail. As it has been discussed, the intention of building an intelligent agent that can participate on the TAC SCM game has been reached with Tiancalli07. How the intended mechanisms worked in order to obtain this intelligent behavior, is one of the goals of the current paper. To prove the thesis that Tiancalli07 works in a more intelligent way than the two previous versions of reactive agents, the following experiment has been developed. Over a local computer with the SCM platform, the three agents have been installed and configured to play one against the others on fifty games. The other three agents participating are dummy agents, in order to not alter the basic results –it has been tested that when many agents are running on a local platform, the messages are not well received by the agents. The average results are presented on Table 1. The following lines are a discussion of the obtained results on the experiment. • The agent that receives more money from its customers is Tiancalli 2005, closely followed by Tiancalli07. However, Tiancalli 2005 is the agent which expenses more money to assemble its computers. This can be explained as follows: the agent of 2007 has improved its mechanisms to purchase components with lower prices, and sell computers to higher prices. This difference can be explained by consulting the parameter “Ratio S/P” that is intended as the amount of incomes divided against the amount of expenses, or in single words, the benefit that the agent receives for each $1 that it invests. Curiously, this is the only important parameter where Tiancalli06 is over Tiancalli 2005. • The agent Tiancalli07 remains more time on the competition with a positive balance. This is also reflected on the final result of the competition, where the same agent also takes the lead. • The amount for storing components and computers is the highest on the agent of 2007. It may be explained by considering that the agent risks more the prices to the customers, and many more computers remain in the current inventory. Also a huge problem for this agent is that when purchasing components, the market tends to offer more components of one type instead of another –for example, there are more processors than motherboards. The absence of one type of component will lead to have a big inventory during days –the days that will take to the agent to get the absent items, and so the storage costs will be higher.

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Agent

Tiancalli 2005 Tiancalli06

Revenue

91,535,200.85

33,321,284.62 85,679,710.08

Interest

142,610.15

82,597.46

Material

78,282,031.77

25,593,433.46 56,140,179.23

Storage

1,181,238.23

647,656.08

1,841,481.00

Penalty

288,055.54

0.00

0.00

Penalty%

0.4%

0

0

Ratio S/P

115%

127%

148%

Result

11,926,485

7,162,793

28,040,241

Orders

3,976

1,833

5,122

Utilization

73%

24%

56%

Delayed

27

0

0

Missed

8

0

0

100%

100%

Del. Performance 99%

Tiancalli07 342,191.54

Table 1. Results of the experiment of fifty simulations between Tiancalli and dummy agents.

•

•

•

•

•

The only agent that receives penalties for late or missed deliveries is the 2005 agent. As it is shown, the amount of penalties represents less than 1% of the total outcomes that the agent makes during the game. It is not an important quantity, however, while it is kept as low or null as possible. The difference on the final result of an average game of Tiancalli07 is twice and a bit more of the result of Tiancalli 2005. The mechanisms implemented on Tiancalli06 allow it to obtain better prices than the agent of 2005; however the amount of components acquired is not enough to produce computers. The agent of 2007 obtains more orders, but uses the factory less than Tiancalli 2005. This is possible by consulting the formula that Tiancalli 2005 uses to determine the potential customers; it tends to accept customers which require a lot of computers –but also with a high amount of money to be paid. Then the agent will require assembling more computers. The case with Tiancalli07 is that the agent takes all the possible requests which may generate an income, so the quantity requested is not so important; then the agent may satisfy more orders without caring the requested quantity of computers. It may be discussed that “Tiancalli06 was an evolution over Tiancalli 2005” due to the poor obtained results. However, an important argument to defend this hypothesis is the ratio of sells against purchases, where it is clearly noticeable that Tiancalli06 obtains more money per invested unit than the agent from 2005. The mechanisms for purchasing components were not substantially different on Tiancalli06, but it is possible that maybe installing SPSv3 on Tiancalli06 the results obtained would be better. On the experiment, it is important to notice that in most of the simulations, Tiancalli07 obtained the first place; however, just in four simulations, the agent descended to second or even fourth place. This argument can be justified with the slow learning curve that the agent performs for new prices and conditions of the market. Tiancalli 2005 obtained only one second, two fourth places and fifth place in the simulations, and Tiancalli06 always obtained sixth place.


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Fig. 3. The balance of the bank account of Tiancalli 2005 (top graph), Tiancalli06 (middle graph) and Tiancalli07 (lower graph). The vertical lines show the moment when the agent has a positive balance.

•

On Figure 3, an example of a simulation is shown, and it displays the balance during the simulation time. The vertical line shows the moment when the agent passes the negative balance, and surprisingly, the agent that achieves this first is Tiancalli06, on day 42. Both Tiancalli 2005 and Tiancalli07 obtain the positive balance over 50 days of competition. However, in the graph can be seen that the agent from 2007 has a more constant line than the other agents, and Tiancalli06 has the most erratic line, due to the lack of components during most of the competition.

8. Conclusions and future work This paper presents the deepest analysis about the construction of the Tiancalli agents since 2005. It intends to describe all the effort that has been developed and both the evolution and experience acquired during the three years participating on this competition, the TAC SCM. It is remarkable that the agent concepts have evolved from Tiancalli 2005 to Tiancalli07. Certainly, the results achieved with the latest version of the agent are far from the previous results; however during the last competition these results could not be reflected on the reached place of the competition. Several considerations and facts that are planned for the following versions of the Tiancalli agent are the following:

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•

•

• • •


The proposed learning curve for the learning structures –but specially the tree- is too slow, and many more simulations must be done in order to obtain an agent with enough knowledge. If new conditions are added and the structure changes, it only affects to the leaves that participated on the price proposal, the others remain static. So, in order to promote the learning capabilities of the agent, several modifications about the knowledge update must be conducted. There is a lot of work done on the pricing system for customers; however the prices for suppliers are the most common issue and the penalties for storing too many useless components –referring to “useless” because they can not be used to assemble any computer due to absence of another component- are still high. There must be an improvement on the IS structure that intends to get the missing components as soon as possible in order to avoid important penalties. There must be an incentive to produce more computers on the factory. It is used the half of the total capacity. Finally, these improvements should be noticed on the competition, reaching even better results. There is still a lot of work to do about the TAC SCM. It is expected that this work serves to promote and invite researchers and universities to participate on the competition. To review more information and results of the Trading Agent Competition and all the contests, it is suggested to check the website: http://www.sics.se/tac

9. References Arunachalam, R., & Sadeh, N., (2004). The supply chain trading agent competition. Electronic Commerce Research and Applications 4, 2005. pp. 63–81. Revised and extended version of paper at AAMAS-04 Workshop on Trading Agent Design and Analysis, New York, 2004. Arunachalam, R., et al (2003), Design of the Supply Chain Trading Competition, IJCAI-03 Workshop on “Trading Agent Design and Analysis”, Mexico, August 2003. He, M., et al, (2006). Designing a successful trading agent for supply chain management. In: Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-06), pp. 1159–1166, Hakodate, Japan, 2006. Macías, D., et al (2005). Tiancalli: An Agent for the Supply Chain Management Game 2005. La Computación en Puebla en el Siglo XXI, pp. 11-16, ISBN, Puebla, México, November 2005. BUAP FCC, Puebla. Macías, D., et al (2006,1) Statistic Analysis for the Tiancalli Agents on TAC SCM 2005 and 2006. In Proceedings of the 15th International Conference on Computing (CIC’2006), pp. 161-166, Mexico City, Mexico, November 2006, ISBN, IEEE Mexico, Macías, D., et al (2006,2). Tiancalli06: An Agent for the Supply Chain Management Game 2006, Proceedings of the International Conference on Computational Intelligence for Modelling, Control & Automation -CIMCA 2006, published on CD-ROM, ISBN, NSW, Australia, November 2006., IEEE, Sydney, Australia. Macías, D., et al (2007) Desarrollo de Agentes Inteligentes para la TAC SCM 2007 (Development of Intelligent Agents for the TAC SCM 2007), Actas de la XII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA-TTIA 2007), pp. 81-90, ISBN, Salamanca, Spain, November 2007, University of Salamanca, Spain.

12 A Framework and Key Techniques for Supply Chain Integration Yanfang Huo1, Xinyue Jiang1, Fu Jia2 and Bingguang Li3

of Management, Tianjin University School of Management, Cranfield University, 3Byrd School of Business, Shenandoah University, 1P.R. China 2UK 3USA

2Cranfield

1School

1. Introduction Supply chain integration is a new kind of organizational model, taking dynamic alliance of supply chain as a subject, to realize global resource integration through interactive collaborate operation of supply chain. Different from vertical integration, this integration focuses on the seamless connection of firms to improve the whole supply chain competitiveness by establishing and maintaining a long-term strategic partnership based on information integration, function and business reengineering, organization integration, cultural adaptation and strategic resources reorganization, etc (Chen & Ma, 2006). Erengue (1999) brought forward four valuable research fields of supply chain, among them 3 are relevant to supply chain integration, i.e. integrated approaches to managing inventory decisions at all stages of the supply chain, the use of information sharing in a multi-partner supply chain, and analytical and simulation models that integrate the three major stages of supply chains which he thought to be is an important future direction of research in the area of supply chain. The literature suggests different theoretical models to describe and operationalise integration. Hammel & Kopczak (1993) pointed out that supply chain life cycle is a whole process from the construction to disaggregation of supply chain and defined the processes of construction, operation and disaggregation from the perspective of focal firm. Fisher (1997) and Nissen (2001) investigated supply chain integration from the aspect of product and agent-based separately. More recently, Lalwani & Mason (2004) and Mason & Lalwani (2006) use a model to characterize the extent of integration in relation to TPL providers. Tang & Qian (2008) established a PLM framework to enable supplier integration and partnership management in the automotive development process. In connection with a survey of the relation between supply chain collaboration and logistics service performance (Stank et al., 2001), a framework for establishing the degree of internal and external collaboration is set up. This framework is further developed by Gimenez & Ventura (2005) in order to study internal and external integration as well as the influence of integration on performance. Again, the framework is appealing, but does not include a more systematic and detailed description of the specific tasks and processes involved in the

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cooperation. An operationalisation of the integration concept requires the identification of both the most essential tasks to be solved in connection with supply chain management and the underlying activities to be carried out to accomplish these tasks (Mortensen et al, 2008). In this paper, investigating the methods of supply chain integration for manufacturing industry in the background of China, a three-echelon theoretical framework for supply chain integration based on Thorn’s model (Thorn, 2002) is established. And then the relative techniques are presented in each level, which will be illustrated as following.

2. The three-echelon theoretical framework for supply chain integration In order to integrate supply chain effectively, based on the comprehensive hierarchical planning framework by Thorn, we establish a theoretical framework for supply chain integration. According to the framework, the key relative techniques can be sorted into three echelons based on the rules from entity objectives to relative objectives and from basic capabilities to advanced capabilities- the basic operations management level, the planning and controlling level and the strategic management level. The integration in operations management level. The supply chain operations level involves the whole process from the material acquisition to order fulfillment, which is the physical level and basic elements of supply chain. The supply chain integration must begin with the integration of this level, which is the basis of collaboration between all the firms. According to the organizations and the functions, the integration in the operational management level includes internal integration of the focal manufacturer, the supplier integration, the distributor integration and the customer integration. The integration in planning and controlling level. The excellent operation needs the support of integrated planning and performance evaluation, which involves the utilization of multiply techniques to plan, control, assess and improve performance. The integration in planning and controlling level coordinates all the business processes, esp. source, make, order fulfillment and inventory replenishment by information utilization and coordination. The core competencies in this level involve: databases, which enable the members to share necessary information; transaction system, which can initiate and deal with inventory replenishment and customer order fulfillment. Besides, it is vital to form the capability relevant to internal communication and collaborative operation. IT-based CPFR strategy is helpful to forming the core competencies. Meanwhile, it is also vital to monitor the business process through performance evaluation and improve the integrated performance continuously. Therefore, the key elements in planning and controlling level can be summed up as IT-based CPFR strategy and performance evaluation. The integration in strategic management level. Out of question, successful supply chain integration needs partnership and management skills to maintain the partnership, while the skills always come from unique organization culture which is the basis of partner selection and maintaining. Thereby partnership maintaining and cultural adaptation are the two kernel elements in strategic management level. Fig. 1 shows the framework and the key elements in every level for supply chain integration. On the basis of core competencies are integrated well with support capacities, the focal firm can coordinate four kinds of flows- product/service flow, knowledge flow, information flow and fund flow – to produce value. The product/service flow refers to the serials of value-adding activities of product/service from material acquisition to end customers. The values add to the product when the product

A Framework and Key Techniques for Supply Chain Integration

217

Fig. 1. The three-echelon theoretical framework for supply chain integration flows along the supply chain and experiences physical changes, packaging, launch, customization, service support and other relative activities until meet the needs of end customers. The knowledge flow is a reverse flow from end customers to suppliers. It always involves the exchange information about sales mode and product description, such as the customization requests, POS data, consumption information of end customers, warehouse and shipment information, etc. The information is vital to supply chain planning because it is helpful for the members to know the sales status well and then reach consistent understanding on customer requirements and consumption status. On the basis, better plans are formulated and the supply chain can work collaboratively. The Information flow is a kind of inter-communicational flow between supply chain members. The information always includes forecast information, promotional plan, purchase order, order confirmation, shipment and inventory information, invoice, payment and replenishing requirements, etc. The information exchange can trigger, control and record the flow of product/service. With the ICT development, more and more information are exchanged by EDI and network instead of paperwork. The fund flow always moves against the value-adding activities. Capital turnover and return on assets are the two main financial metrics which are of great importance to the supply chain performance. The four flows exist always even if no coordination in the supply chain. However，under the circumstance of low integration and bad coordination, the flows move unsmooth which will result in delay, redundancy and inefficiency. While, integrated supply chain will accelerate the flows, with which the supply chain can produce maximum customer value and keep in a good condition meanwhile.

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3. Key elements in the operational management level The operation management level is the level aiming at synchronous operation of supply chain. The key issue in this level is how to balance and coordinate the restrictions, such as resources, information, capacity and time, through integration and coordination within each firm and between firms. The integration in operations level of supply chain can be illustrated in two dimensions: internal integration and external collaboration, shown in Figure 2. In the following, we will illuminate them and the key elements separately.

external collaboration

low

high

Supply chain operation

Collaborative supply chain operation

internal operation

Collaborative internal operation

low

high

Internal integration Fig. 2. The operational integration matrix 3.1 Internal integration and collaborative internal operation of within focal firm The internal integration, the function to function integration within the focal firm, is the first step of operations integration, and also the basis of to success of supply chain integration. High internal integration can reach a level of “collaborative internal operation”, with which the whole firm works like an integrated system that results in better performance and better interdepartmental effectiveness, such as cycle time reduction, better in-stock performance, increased product availability levels, and improvement in order-to delivery lead times (Harrison et al, 2008). Moreover, high internal integration is also the foundation of high external integration. A study of Spanish food manufacturers by Gimenez (2006) shows that the highest levels of external integration are achieved by firms which have already achieved the highest levels of internal integration between logistics, production and marketing. For the internal integration is process-oriented, the firms need to come across the border of functions to build a borderless flat organization through BPR (business process reengineering) combined with ICT-based advanced production modes, such as MRPII (manufacturing resources planning), ERP (enterprise resources planning), lean production, agile manufacturing, concurrent engineering, etc. BPR is the fundamental approach for internal integration within the focal firm, which emphasizes ‘... the fundamental rethinking and radical redesign of business processes to achieve dramatic improvements in critical contemporary measures of performance, such as cost, quality, service, and speed.’ To implement BRP effectively, Hammer (1990) presents seven principles for BPR: organize around outcomes, not tasks; have those who use the output of the process perform the process; subsume information-processing work into the real work that produces the information; treat geographically dispersed resources as though


219

they were centralize; link parallel activities instead of integrating their results; put the decision point where the work is performed, and build control into the process; capture the information once and at the source. Base on these principles, cross-functional team, first presented by Toyota in lean production, can be one of the rational reference models for internal integration. While before the actual efforts of new model, the firms should define some mechanisms and actions to monitor and evaluate the status of collaboration, and then improve the initiative planning process. ‘Alignment Compass’ (van Hoek & Mitchell, 2006), the alignment analysis tool shown as Figure 3, illustrates four areas where alignment improvement efforts could be focused: in the interactions with peers from other functions, in interactions with their bosses and the Board, in interactions with their teams, and in their own day-to-day behavior.

Fig. 3. Alfa Laval’s alignment compass (van Hoek and Mitchell, 2006) When the focal firm finished internal integration, it must follow some rules as below to carry out the new business model successfully: Rule 1 Transfer from functional management to process management. The new model is processoriented, and put the decision point where the work is performed, therefore response to market and customer will be improved through the shortened communication channel and time.

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Rule 2 Focus on systematic philosophy about whole process optimization. Reengineer and optimize the business process to delete the useless activities or non-value-added activities, meanwhile, make each activity add the maximum value to customers. Note that all of these are base on global optimization, not local optimization, aiming at eliminating the selfish departmentalism and advantages equalitarianism. Rule 3 Build a flat organization. Design processes first, and then build the organization based on processes. Remove the middle-level managers as could as possible. Rule 4 Have everybody play his important role in the whole business process. Each person who processes the business should have comprehensive qualification and teamwork spirit. At the same time, the organization should build a new mechanism for self-learning. Rule 5 Integrate business processes oriented to customers and suppliers. In the age of competition between supply chains, the firm should consider not only the collaboration between internal business processes, but the redesign of the interfaces between the focal firm with its customers and suppliers, when implementing BPR. Rule 6 Resolve the conflicts between dispersed business and centralized management using ICT. When designing and improving the business processes, the firm should make the most use of ICT to process and share information as far as possible, convert sequencing processes into synchronous ones, resolve the conflicts between separated businesses with centralized management. All in all, the firm can provide the right products with lowest costs and accurate amounts at right time and right place through BPR and integration of internal core businesses. Furthermore, high internal integration will improve the firm’s decision-making capability dramatically, so the firm can capture the opportunities and win the competition in the fierce market. 3.2 External collaboration and collaborative supply chain operation The second dimension of operational integration is called external integration, or inter company integration, referring to the cross-border operational integration in the supply chain which can place customer and supplier processes closer together. Compared with internal integration, external integration is a relative new concept, which integrates a firm’s logistics with external logistics of suppliers and customers by the excellent collaboration between the partners. High external integration has some features like: increased logistics transactions with suppliers and customers; increased logistics collaboration between the focal firm with their customers and suppliers; more indistinct organizational boundary between partners in logistics collaboration. External integration makes the supply chain operate like a real physical entity to gain more powerful competitive advantage. High external integration can be divided into ‘supply chain operation’ and ‘collaborative supply chain operation’ based on the internal integration level of each firms. The former one is high external integration with low internal external integration (In fact, it rarely exists). The latter one is a real high integration type base on high internal integration and high external integration. High-integration supply chain operates in a form of virtual organization, which is like a physical entity with high competency. External integration can be divided into three basic types according to the partner along the material flow - supplier integration, distributor integration and customer integration, which will be explained in detail as following.


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3.2.1 Supplier integration Supplier integration plays a very important role in the operational integration of supply chain. One of the keys to increased responsiveness in the supply chain is a high-integration with upstream suppliers. Therefore the focal firm should pay more attentions on supplier development and integration to build partnership with the suppliers, which can increase the firm’s performance or capabilities and meet short-term or long-term supply needs of buyers. Based on PDCA (Plan-do-check-action) cycle, we build a model– supplier integration cycle to support the activity of supplier integration. The model can be divided into 5 stages around with the goal of supplier development and excellent performance. At the initial stage, the firm should set up clearly and consistent objectives, which must communicate with the suppliers carefully. At the second stage, the firm should select qualified suppliers and establish a perfect evaluation system and assess the supplier performance based on the evaluation system. At the third stage, feed back the actual performance to the suppliers. While at the fourth state, the suppliers analyze the process to find the performance gap, and then formulate plans to improve their performance. Finally, the focal firm admits suppliers’ performance and gives them the relative treatments according to their performance to support joint development. The supplier integration cycle is shown as Figure 4. In the following we will illustrate how the cycle operates based on John Deere’s practices.

Fig. 4. The supplier integration cycle A. Objective setting. The first step of supplier integration is to set the consistent objectives and strategies, on the basis, integrate information, processes and resources to realize quick response to customer needs. The objectives of supplier integration always involve: Build a win-win relationship with supplier, share the resources each other, and achieve continuous improvement (CI) to win the competitive advantage in the marketplace. Control the total cost by quality improvement combined with cost management. Build effective performance measurement system to lead and encourage CI, and communicate with suppliers on their performance timely and accurately. Meanwhile, provide standards of supplier admission for their excellent performance. On the basis, carry out strategic sourcing. Encourage the suppliers to involve in all the core processes and make full use of their technical supports, innovations and experiences to improve the capability and competitiveness of the whole chain.

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B. Supplier evaluation. Often, the suppliers need to be evaluated from such aspects as quality, delivery, cost, technical support and collaboration, to determine their relationship with the focal firm. The contents of these metrics are illustrated as below. Quality, the metrics on the capability that suppliers’ quality management system and material provided meet the requirements, expectation and material quality of the focal firm. This metrics, involving quality PPM and quality effects, provides suppliers with the statistical information on their products/service quality. Delivery. Delivery evaluation provides the statistical basis for suppliers with their order fulfilling capability based on order amounts and delivery date. The delivery level is denoted by PPM (Delivery PPM = number of defects/supplied amount×106), which is calculated based on the amounts of early delivery, late delivery or over delivery. Technical support, the metrics on suppliers’ knowledge and their capability of technology application, to determine whether they can support strongly the focal firm’s needs of product development and manufacturing. The performances of suppliers in such fields as the product techniques and innovations, the delivery process, the manufacturing process, the time of manufacturing critical path, and the warranty are involved in the evaluation system. Collaboration, comprehensive analysis of suppliers’ initiative, attitude, responsiveness, communication, detail-concerned, and safety performance. The evaluation fields always include information share, problem-solving, responsiveness to customer requirements, business relations, consistency of processes, and the quality and smoothness of electric commercial. Cost, the metrics on the capabilities of supplier in price competitiveness, cost control and reduction. The evaluation objects include the cost reduction planning, the net cost reduction performance, the enthusiasm in cost control and the performance during product delivery. As for the collaborative relationship, according to the supplier performances, it can be divided into four grades as following. Partners, the suppliers whose performances have topped the performance measurements of the focal firm and reach the world class level, and played an important role to customers satisfaction of the focal firm. Key suppliers, the suppliers whose performance have topped the lowest level of the focal firm and are go further towards the world class level continuous. Qualified suppliers, the suppliers whose performances reach the lowest level of the focal firm, but no action on continuous improvement. In questioned suppliers, the suppliers whose performances are lower than the lowest level of the focal firm and may be rejected from the supplier group. The final level is determined by the lowest one of any field and the supplier performance in each field is assessed by cross-functional team. The evaluation system is shown in Table 1. C. Feedback and improvement. After evaluation, the focal firm will give the suppliers feedback on their performances. Besides the information of past and current performances, the status of other key suppliers is also presented in the report to encourage the supplier’s improvement. Moreover, it is prescribed that the suppliers those achieve a 50% improvement in some field (quality, delivery, etc) than the early year can be upgraded to the upper one level (e.g. from qualified supplier to key supplier, but never from key supplier to partner). It is a measure to encourage the CI activities of suppliers.


Quality

Delivery

Partner 92%-100% Excellent quality, topped in all metrics; high reliable product/service; wellrecorded and filed quality plan & improvement measures; excellent outcomes in continuous improvement Always deliver product/service on-time; actively respond to the short-time order; seamless link of delivery process to pricing process.

Technical Respond to technical support problems and service problems promptly; explore and implement innovation in techniques; provide advanced techniques for customers; service representatives are fully-trained and are the expert of their field.

Collaboration

Cost

Care for customers’ experiences; make great efforts to continuous improvement actively; provide accurate information over expectation; be good at teamwork and communication; clear, open and frank business relations Have set goals of continuous cost reduction and monitor its operations; focus on inner cost, make great efforts to reduce the cost both internally and externally with suppliers; keep the most competitive price always.

Key supplier 80%-91% Excellent quality plan; timely response to quality problems; reliable product/service; better outcomes than measurements consistently

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Qualified supplier 70%-79% Quality meets the expectation; passive response to quality problems; substantially satisfied by inner customer; approved but not implemented collaboration agreement Can deliver Always be reminded of product/service onproduct/service delivery; sometimes time and respond to the short-time order; a need to be followed on few pricing problems. the problems of delivery and/or price; respond passively with no expectation Rarely go wrong when Reply to technical introduce new or change passively (not existing product/ actively); sometimes service; service new products or new representatives are able services are unusable; to solve most need to be followed to techniques or service implement innovation problems; make efforts in technique sufficient to implement technical support. innovation in techniques under proper directions. Support its staff and Need to be traced; worker fully; outcome- respond to customers oriented to meet the requirements passively expectations all the with no prediction; time; com in on share information problem-solving sufficiently actively.

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